LEGO-Net: Learning Regular Rearrangements of Objects in Rooms
Open accessAbstract
Humans universally dislike the task of cleaning up a messy room. If machines were to help us with this task, they must understand human criteria for regular arrangements, such as several types of symmetry, co-linearity or co-circularity, spacing uniformity in linear or circular patterns, and further inter-object relationships that relate to style and functionality. Previous approaches for this task relied on human input to explicitly specify goal state, or synthesized scenes from scratch - but such methods do not address the rearrangement of existing messy scenes without providing a goal state. In this paper, we present LEGO-Net, a data-driven transformer-based iterative method for LEarning reGular rearrangement of Objects in messy rooms. LEGO-Net is partly inspired by diffusion models - it starts with an initial messy state and iteratively “de-noises” the position and orientation of objects to a regular state while reducing distance traveled. Given randomly perturbed object positions and orientations in an existing dataset of professionally-arranged scenes, our method is trained to recover a regular rearrangement. Results demonstrate that our method is able to reliably rearrange room scenes and outperform other methods. We additionally propose a metric for evaluating regularity in room arrangements using number-theoretic machinery.
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Many works on scene design learn spatial knowledge priors from existing 3D scene databases or leverage user input and refine the 3D scene iteratively.
“Many works on scene design either learn spatial knowledge priors from existing 3D scene databases [4, 31, 49-51, 54, 59] or leverage user input and refine the 3D scene iteratively [3, 5].”
From Holodeck: Language Guided Generation of 3D Embodied AI Environments (2024) · cites this paper as [49]
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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · see citing works
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 48,293 · see citing works
Image-to-Image Translation with Conditional Adversarial Networks
Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.632 · Cited by 22,930 · see citing works
Artificial intelligence: a modern approach
Kumar, D. A., Balasubramanian, S., Byeon, D. H., & Manerkar, P. G. V. (1995) · Choice Reviews Online · doi:10.5860/choice.33-1577 · Cited by 22,182 · see citing works
High-Resolution Image Synthesis with Latent Diffusion Models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01042 · Cited by 15,880 · see citing works
Learning Transferable Visual Models From Natural Language Supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al. (2021) · arXiv · doi:10.48550/arxiv.2103.00020 · Cited by 15,293 · see citing works
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Charles, R., Su, H., Mo, K., & Guibas, L. J. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.16 · Cited by 9,767 · see citing works
Self-citationDenoising Diffusion Probabilistic Models
Ho, J., Jain, A., & Abbeel, P. (2020) · arXiv · doi:10.48550/arxiv.2006.11239 · Cited by 8,712 · see citing works
Extracting and composing robust features with denoising autoencoders
Vincent, P., Larochelle, H., Bengio, Y., & Manzagol, P.-A. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390294 · Cited by 7,436 · see citing works
The Earth Mover's Distance as a Metric for Image Retrieval
Rubner, Y., Tomasi, C., & Guibas, L. (2000) · International Journal of Computer Vision · doi:10.1023/a:1026543900054 · Cited by 5,547 · see citing works
Self-citationDiffusion Models Beat GANs on Image Synthesis
Dhariwal, P., & Nichol, A. (2021) · arXiv · doi:10.48550/arxiv.2105.05233 · Cited by 3,362 · see citing works
Zhang, R., Isola, P., & Efros, A. A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46487-9_40 · Cited by 3,361 · see citing works
Taming Transformers for High-Resolution Image Synthesis
Esser, P., Rombach, R., & Ommer, B. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01268 · Cited by 2,514 · see citing works
Score-Based Generative Modeling through Stochastic Differential Equations
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., & Poole, B. (2020) · arXiv · doi:10.48550/arxiv.2011.13456 · Cited by 1,953 · see citing works
RePaint: Inpainting using Denoising Diffusion Probabilistic Models
Lugmayr, A., Danelljan, M., Romero, A., Yu, F., Timofte, R., & Van Gool, L. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01117 · Cited by 1,778 · see citing works
Palette: Image-to-Image Diffusion Models
Saharia, C., Chan, W., Chang, H., Lee, C., Ho, J., Salimans, T., Fleet, D., & Norouzi, M. (2022) · Special Interest Group on Computer Graphics and Interactive Techniques Conference Proceedings · doi:10.1145/3528233.3530757 · Cited by 1,717 · see citing works
Generative Modeling by Estimating Gradients of the Data Distribution
Song, Y., & Ermon, S. (2019) · arXiv · doi:10.48550/arxiv.1907.05600 · Cited by 1,453 · see citing works
GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
Nichol, A., Dhariwal, P., Ramesh, A., Shyam, P., Mishkin, P., McGrew, B., Sutskever, I., & Chen, M. (2021) · arXiv · doi:10.48550/arxiv.2112.10741 · Cited by 1,357 · see citing works
On the Continuity of Rotation Representations in Neural Networks
Zhou, Y., Barnes, C., Lu, J., Yang, J., & Li, H. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00589 · Cited by 1,255 · see citing works
Training Generative Adversarial Networks with Limited Data
Karras, T., Aittala, M., Hellsten, J., Laine, S., Lehtinen, J., & Aila, T. (2020) · arXiv · doi:10.48550/arxiv.2006.06676 · Cited by 1,170 · see citing works
A Connection Between Score Matching and Denoising Autoencoders
Vincent, P. (2011) · Neural Computation · doi:10.1162/neco_a_00142 · Cited by 1,011 · see citing works
High-Resolution Image Synthesis with Latent Diffusion Models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2021) · arXiv · doi:10.48550/arxiv.2112.10752 · Cited by 737 · see citing works
AI2-THOR: An Interactive 3D Environment for Visual AI
Kolve, E., Mottaghi, R., Han, W., VanderBilt, E., Weihs, L., Herrasti, A., Deitke, M., Ehsani, K., Gordon, D., Zhu, Y., et al. (2017) · arXiv · doi:10.48550/arxiv.1712.05474 · Cited by 578 · see citing works
SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation
Wang, W., Yu, R., Huang, Q., & Neumann, U. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00272 · Cited by 546 · see citing works
Example-based synthesis of 3D object arrangements
Fisher, M., Ritchie, D., Savva, M., Funkhouser, T. A., & Hanrahan, P. (2012) · ACM Transactions on Graphics · doi:10.1145/2366145.2366154 · Cited by 342 · see citing works
GSPN: Generative Shape Proposal Network for 3D Instance Segmentation in Point Cloud
Yi, L., Zhao, W., Wang, H., Sung, M., & Guibas, L. J. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00407 · Cited by 323 · see citing works
Self-citationInteractive furniture layout using interior design guidelines
Merrell, P. C., Schkufza, E., Li, Z., Agrawala, M., & Koltun, V. (2011) · ACM Transactions on Graphics · doi:10.1145/2010324.1964982 · Cited by 269 · see citing works
3D-FRONT: 3D Furnished Rooms with layOuts and semaNTics
Fu, H., Cai, B., Gao, L., Zhang, L.-X., Wang, J., Li, C., Zeng, Q., Sun, C., Jia, R., Zhao, B., et al. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01075 · Cited by 267 · see citing works
Yu, L.-F., Yeung, S.-K., Tang, C., Terzopoulos, D., Chan, T. F.-C., & Osher, S. (2011) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/1964921.1964981 · Cited by 254 · see citing works
Metropolis procedural modeling
Talton, J. O., Lou, Y., Lesser, S., Duke, J., Měch, R., & Koltun, V. (2011) · ACM Transactions on Graphics · doi:10.1145/1944846.1944851 · Cited by 229 · see citing works
CLIPort: What and Where Pathways for Robotic Manipulation
Shridhar, M., Manuelli, L., & Fox, D. (2021) · arXiv · doi:10.48550/arxiv.2109.12098 · Cited by 223 · see citing works
Deep convolutional priors for indoor scene synthesis
Wang, K., Savva, M., Chang, A. X., & Ritchie, D. (2018) · ACM Transactions on Graphics · doi:10.1145/3197517.3201362 · Cited by 209 · see citing works
Li, M., Patil, A. G., Xu, K., Chaudhuri, S., Khan, O., Shamir, A., Tu, C., Chen, B., Cohen‐Or, D., & Zhang, H. (2019) · ACM Transactions on Graphics · doi:10.1145/3303766 · Cited by 186 · see citing works
Human-Centric Indoor Scene Synthesis Using Stochastic Grammar
Qi, S., Zhu, Y., Huang, S., Jiang, C., & Zhu, S. C. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00618 · Cited by 162 · see citing works
Deep Generative Modeling for Scene Synthesis via Hybrid Representations
Zhang, Z., Yang, Z., Ma, C., Luo, L., Huth, A. G., Vouga, E., & Huang, Q.-X. (2018) · ACM Transactions on Graphics · doi:10.1145/3381866 · Cited by 155 · see citing works
Fast and Flexible Indoor Scene Synthesis via Deep Convolutional Generative Models
Ritchie, D., Wang, K., & Lin, Y.-A. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00634 · Cited by 151 · see citing works
SceneFormer: Indoor Scene Generation with Transformers
Wang, X., Yeshwanth, C., & Nießner, M. (2021) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv53792.2021.00021 · Cited by 143 · see citing works
Habitat 2.0: Training home assistants to rearrange their habitat
Szot, A., Clegg, A., Undersander, E., Wijmans, E., Zhao, Y., Turner, J., Maestre, N., Mukadam, M., Chaplot, D., Maksymets, O., et al. (2021) · arXiv · doi:10.48550/arxiv.2106.14405 · Cited by 143 · see citing works
Synthesizing open worlds with constraints using locally annealed reversible jump MCMC
Yeh, Y.-T., Yang, L., Watson, M., Goodman, N. D., & Hanrahan, P. (2012) · ACM Transactions on Graphics · doi:10.1145/2185520.2185552 · Cited by 136 · see citing works
GPU-based simulation of cloth wrinkles at submillimeter levels
Wang, H. (2021) · ACM Transactions on Graphics · doi:10.1145/3450626.3459787 · Cited by 118 · see citing works
Push planning for object placement on cluttered table surfaces
Cosgun, A., Hermans, T., Emeli, V., & Stilman, M. (2011) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2011.6094737 · Cited by 117 · see citing works
Rearrangement: A Challenge for Embodied AI
Batra, D., Chang, A. X., Chernova, S., Davison, A. J., Deng, J., Koltun, V., Levine, S., Malik, J., Mordatch, I., Mottaghi, R., et al. (2020) · arXiv · doi:10.48550/arxiv.2011.01975 · Cited by 109 · see citing works
Bukowski, R. W., & Séquin, C. H. (1995) · ACM Symposium on Interactive 3D Graphics (I3D) · doi:10.1145/199404.199427 · Cited by 104 · see citing works
SDEdit: Image Synthesis and Editing with Stochastic Differential EquationsPreprint
Meng, C., Song, Y. S., Song, J., Wu, J., Zhu, J.-Y., & Ermon, S. (2021) · arXiv (Cornell University) · Cited by 103 · see citing works
Mechanical Search: Multi-Step Retrieval of a Target Object Occluded by Clutter
Danielczuk, M., Kurenkov, A., Balakrishna, A., Matl, M., Wang, D., Martín-Martín, R., Garg, A., Savarese, S., & Goldberg, K. Y. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8794143 · Cited by 102 · see citing works
A Polynomial Time, Numerically Stable Integer Relation Algorithm
Ferguson, H. R. P., Bailey, D. H., & Kutler, P. (1998) · Cited by 99 · see citing works
Weihs, L., Deitke, M., Kembhavi, A., & Mottaghi, R. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00586 · Cited by 98 · see citing works
Rearrangement planning using object-centric and robot-centric action spaces
King, J. E., Cognetti, M., & Srinivasa, S. S. (2016) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2016.7487583 · Cited by 86 · see citing works
Generative Layout Modeling using Constraint Graphs
Para, W. R., Guerrero, P., Kelly, T., Guibas, L. J., & Wonka, P. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00662 · Cited by 86 · see citing works
Self-citationConstraint-Based Automatic Placement for Scene Composition
Xu, K., Stewart, J. H., & Fiume, E. L. (2002) · Proceedings · doi:10.20380/gi2002.04 · Cited by 86 · see citing works
SceneGraphNet: Neural Message Passing for 3D Indoor Scene Augmentation
Zhou, Y., While, Z., & Kalogerakis, E. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00748 · Cited by 81 · see citing works
Practical pushing planning for rearrangement tasks
Ben‐Shahar, O., & Rivlin, E. (1998) · IEEE Transactions on Robotics and Automation · doi:10.1109/70.704220 · Cited by 80 · see citing works
Monte-Carlo Tree Search for Efficient Visually Guided Rearrangement PlanningPreprint
Labbé, Y., Zagoruyko, S., Kalevatykh, I., Ivan Laptev, A., Carpentier, J., Aubry, M., & Šivic, J. (2019) · arXiv (Cornell University) · Cited by 78 · see citing works
Meta-Sim2: Unsupervised Learning of Scene Structure for Synthetic Data Generation
Devaranjan, J., Kar, A., & Fidler, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58520-4_42 · Cited by 73 · see citing works
An algebraic model for parameterized shape editing
Bokeloh, M., Wand, M., Seidel, H., & Koltun, V. (2012) · ACM Transactions on Graphics · doi:10.1145/2185520.2185574 · Cited by 72 · see citing works
End-to-End Optimization of Scene Layout
Luo, A., Zhang, Z., Wu, J., & Tenenbaum, J. B. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00381 · Cited by 72 · see citing works
ATISS: Autoregressive Transformers for Indoor Scene Synthesis
Paschalidou, D., Kar, A., Shugrina, M., Kreis, K., Geiger, A., & Fidler, S. (2021) · arXiv · doi:10.48550/arxiv.2110.03675 · Cited by 62 · see citing works
Learning Generative Models of 3D Structures
Chaudhuri, S., Ritchie, D., Wu, J., Xu, K., & Zhang, H. (2020) · Computer Graphics Forum · doi:10.1111/cgf.14020 · Cited by 61 · see citing works
StructFormer: Learning Spatial Structure for Language-Guided Semantic Rearrangement of Novel Objects
Liu, W., Paxton, C., Hermans, T., & Fox, D. (2022) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra46639.2022.9811931 · Cited by 59 · see citing works
iGibson 2.0: Object-Centric Simulation for Robot Learning of Everyday Household Tasks
Li, C., Xia, F., Martín-Martín, R., Lingelbach, M., Srivastava, S., Shen, B., Vainio, K., Gokmen, C., Dharan, G., Jain, T., et al. (2021) · arXiv · doi:10.48550/arxiv.2108.03272 · Cited by 58 · see citing works
Computer Simulation of Human Thinking and Problem Solving
Simon, H. A., & Newell, A. (1962) · Monographs of the Society for Research in Child Development · doi:10.2307/1165535 · Cited by 57 · see citing works
Doğar, M. R., Koval, M. C., Tallavajhula, A., & Srinivasa, S. S. (2013) · Autonomous Robots · doi:10.1007/s10514-013-9372-x · Cited by 50 · see citing works
SG-VAE: Scene Grammar Variational Autoencoder to Generate New Indoor Scenes
Purkait, P., Zach, C., & Reid, I. D. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58586-0_10 · Cited by 41 · see citing works
Fast and Scalable Position-Based Layout Synthesis
Weiss, T., Litteneker, A., Duncan, N., Nakada, M., Jiang, C., Yu, L.-F., & Terzopoulos, D. (2018) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2018.2866436 · Cited by 41 · see citing works
Unobservable Monte Carlo planning for nonprehensile rearrangement tasks
King, J. E., Ranganeni, V., & Srinivasa, S. S. (2017) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2017.7989544 · Cited by 38 · see citing works
Rearranging similar objects with a manipulator using pebble graphs
Krontiris, A., Shome, R., Dobson, A., Kimmel, A. S., & Bekris, K. E. (2014) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/humanoids.2014.7041499 · Cited by 36 · see citing works
Combining motion planning and optimization for flexible robot manipulation
Scholz, J., & Stilman, M. (2010) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/ichr.2010.5686849 · Cited by 34 · see citing works
ConDor: Self-Supervised Canonicalization of 3D Pose for Partial Shapes
Sajnani, R., Poulenard, A., Jain, J., Dua, R., Guibas, L. J., & Sridhar, S. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01646 · Cited by 30 · see citing works
Self-citationWang, H., Liang, W. G., & Yu, L.-F. (2020) · ACM Transactions on Graphics · doi:10.1145/3414685.3417788 · Cited by 26 · see citing works
Fast 3D Indoor Scene Synthesis with Discrete and Exact Layout Pattern ExtractionPreprint
Zhang, S., Zhang, S.-K., Xie, W., Luo, C., & Fu, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.00328 · Cited by 10 · see citing works
Synchronized Multi-arm Rearrangement Guided by Mode Graphs with Capacity Constraints
Shome, R., & Bekris, K. E. (2021) · Springer proceedings in advanced robotics · doi:10.1007/978-3-030-66723-8_15 · Cited by 4 · see citing works
IFR-Explore: Learning Inter-object Functional Relationships in 3D Indoor ScenesPreprint
Li, Q., Mo, K., Yang, Y., Zhao, H., & Guibas, L. J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2112.05298 · Cited by 2 · see citing works
Self-citationBERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805
Flagged first, then by citations
6 of the 79 references couldn’t be matched to a known paper.
Research works printed but not linked: 11
- Paul J Besl and Neil D McKay. Method for registration of 3-d shapes. In Sensor fusion IV: control paradigms and data structures, volume 1611, pages 586-606. Spie, 1992. 14
- Mikołaj Bińkowski, Danica J Sutherland, Michael Arbel, and Arthur Gretton. Demystifying mmd gans. arXiv preprint arXiv:1801.01401, 2018. 14 · arXiv
- Richard W Bukowski and Carlo H Séquin. Object associa- tions: a simple and practical approach to virtual 3d manipu- lation. In Proceedings of the 1995 symposium on Interactive 3D graphics, pages 131-ff, 1995. 2
- Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilib- rium. Advances in neural information processing systems, 30, 2017. 14
- Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffu- sion probabilistic models. Advances in Neural Information Processing Systems, 33:6840-6851, 2020. 2, 3, 4, 16
- Alain Hore and Djemel Ziou. Image quality metrics: Psnr vs. ssim. In 2010 20th international conference on pattern recognition, pages 2366-2369. IEEE, 2010. 14
- Manyi Li, Akshay Gadi Patil, Kai Xu, Siddhartha Chaudhuri, Owais Khan, Ariel Shamir, Changhe Tu, Baoquan Chen, Daniel Cohen-Or, and Hao Zhang. Grains: Generative re- cursive autoencoders for indoor scenes. ACM Transactions on Graphics (TOG), 38(2):1-16, 2019. 2
- Rosanne Liu, Joel Lehman, Piero Molino, Felipe Pet- roski Such, Eric Frank, Alex Sergeev, and Jason Yosinski. An intriguing failing of convolutional neural networks and the coordconv solution. Advances in neural information pro- cessing systems, 31, 2018. 16
- Hanqing Wang, Wei Liang, and Lap-Fai Yu. Scene mover: Automatic move planning for scene arrangement by deep reinforcement learning. ACM Transactions on Graphics, 39(6), 2020. 3
- Kai Wang, Yu-An Lin, Ben Weissmann, Manolis Savva, An- gel X Chang, and Daniel Ritchie. Planit: Planning and in- stantiating indoor scenes with relation graph and spatial prior networks. ACM Transactions on Graphics (TOG), 38(4):1- 15, 2019. 2, 6
- Mingdong Wu, Fangwei Zhong, Yulong Xia, and Hao Dong. Targf: Learning target gradient field for object rearrange- ment. arXiv preprint arXiv:2209.00853, 2022. 3 · arXiv
Cited by
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Ding, Y. F., Zhang, X., Paxton, C., & Zhang, S. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10342169 · Cited by 144 · see citing works
“In the past, researchers have equipped mobile manipulators with semantic information using machine learning methods [4], [5], [15], [16].”
BackgroundDiffuScene: Denoising Diffusion Models for Generative Indoor Scene Synthesis
Tang, J., Nie, Y., Markhasin, L., Dai, A., Thies, J., & Nießner, M. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01938 · Cited by 119 · see citing works
DALL-E-Bot: Introducing Web-Scale Diffusion Models to Robotics
Kapelyukh, I., Vosylius, V., & Johns, E. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3272516 · Cited by 99 · see citing works
“For furniture layout generation, there are methods which predict goal states via iterative denoising [14], or avoid collisions during the rearrangement process using gradient fields [15].”
MethodHolodeck: Language Guided Generation of 3D Embodied AI Environments
Yang, Y., Sun, F.-Y., Weihs, L., Vanderbilt, E., Herrasti, A., Han, W., Wu, J., Haber, N., Krishna, R., Liu, L., et al. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.01536 · Cited by 101 · see citing works
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VRCopilot: Authoring 3D Layouts with Generative AI Models in VR
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“We chose this model because it has been used as baseline models for work in the domain of indoor scene synthesis (e.g., [20, 60]).”
MIME: Human-Aware 3D Scene Generation
Yi, H., Huang, C.-H. P., Tripathi, S., Hering, L., Thies, J., & Black, M. J. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01246 · Cited by 51 · see citing works
“ViT [52]), sampling points around the boundary [69], or simply enlarging the feature dimension Figure 8.”
BackgroundLayoutVLM: Differentiable Optimization of 3D Layout via Vision-Language Models
Sun, F.-Y., Liu, W., Gu, S., Lim, D., Bhat, G., Tombari, F., Li, M., Haber, N., & Wu, J. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.02744 · Cited by 38 · see citing works
“Traditional scene synthesis and layout generation meth-ods are often constrained by predefined object categories and patterns of object placements learned from synthetic scene datasets [1–3], preventing them from achieving the diversity of scene layouts seen in the real world.”
I-Design: Personalized LLM Interior Designer
Çelen, A., Guo, H., Schindler, K., Gool, L. V., Armeni, I., Obukhov, A., & Wang, X. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-92387-6_17 · Cited by 40 · see citing works
“LEGO-NET [56] refines initial coarse room layouts by learning human criteria for regularity through a transformer-based diffusion-like pipeline.”
BackgroundSG-Bot: Object Rearrangement via Coarse-to-Fine Robotic Imagination on Scene Graphs
Zhai, G., Cai, X., Huang, D., Di, Y., Manhardt, F., Tombari, F., Navab, N., & Busam, B. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610792 · Cited by 28 · see citing works
“These goal states can be implicitly represented, such as by gradient fields [59], scene distributions [60], or sequential reasoning on the observation [13].”
BackgroundUGG: Unified Generative Grasping
Lu, J., Kang, H., Li, H., Liu, B., Yang, Y., Qi-xing Huang, A., & Hua, G. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72855-6_24 · Cited by 26 · see citing works
“Diffusion models effectively handle spatial connection-based generative challenges, as evidenced by molecular de-sign [75], layout generation [6, 72], gripper pose generation [67] and scene synthesis and planning [20].”
BackgroundAdvances in Data‐Driven Analysis and Synthesis of 3D Indoor Scenes
Patil, A. G., Patil, S. G., Li, M., Fisher, M., Savva, M., & Zhang, H. (2023) · Computer Graphics Forum · doi:10.1111/cgf.14927 · Cited by 21 · see citing works
AnyHome: Open-Vocabulary Generation of Structured and Textured 3D Homes
Fu, R., Wen, Z., Liu, Z., & Sridhar, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72933-1_4 · Cited by 19 · see citing works
Self-citationKim, S., Kim, D., Shin, J.-E., & Woo, W. (2024) · IEEE Conference on Virtual Reality and 3D User Interfaces (VR) · doi:10.1109/vr58804.2024.00099 · Cited by 15 · see citing works
FOREST2SEQ: Revitalizing Order Prior for Sequential Indoor Scene Synthesis
Sun, Q., Zhou, H., Zhou, W., Li, L., & Li, H. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72698-9_15 · Cited by 12 · see citing works
Interactive Interior Design Recommendation via Coarse-to-fine Multimodal Reinforcement Learning
Zhang, H., Sun, Y., Guo, W., Liu, Y., Lu, H., Lin, X. S., & Xiong, H. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3612420 · Cited by 12 · see citing works
“Along these lines, major research efforts are being devoted to generating decoration effects based on textual descriptions [8, 28, 33, 34].”
BackgroundComputer-Aided Layout Generation for Building Design: A Review
Liu, J., Xue, Y., Ni, H., Yu, R., Zhou, Z., & Huang, X. (2025) · Computational Visual Media · doi:10.26599/cvm.2025.9450484 · Cited by 11 · see citing works
Méndez, A., Prados, A., Menéndez, E., & Barber, R. (2024) · IEEE Access · doi:10.1109/access.2024.3422808 · Cited by 11 · see citing works
“Other methods predict continuous target states by iterative de-noising [37], or perform collision avoidance during the reorganisation process using gradient fields as the TarGF algorithm [38].”
MethodLVDiffusor: Distilling Functional Rearrangement Priors From Large Models Into Diffusor
Zeng, Y., Wu, M., Long, Y., Zhang, J., Ding, H., Cheng, H., & Dong, H. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3438036 · Cited by 11 · see citing works
“StructDiffusion [21], StructFormer [22], and RPDiff [30] learn conditional generative models from arrangement examples gen-eratedinsimulationusinghandcraftedfunctions.”
BackgroundMethodSceneFactor: Factored Latent 3D Diffusion for Controllable 3D Scene Generation
Bokhovkin, A., Meng, Q., Tulsiani, S., & Dai, A. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.00067 · Cited by 9 · see citing works
“Most similar to our approach are several recent approaches, DiffInDScene [30], BlockFusion [70], SemCity [34] and XCube [51], which have been developed directly for large-scale scene generation, leveraging more flexible 3D representations unrestricted by object retrieval.”
FairScene: Learning unbiased object interactions for indoor scene synthesis
Wu, Z., Wang, Z., Liu, S., Luo, H., Lu, J., & Yan, H. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110737 · Cited by 8 · see citing works
Chat2Layout: Interactive 3D Furniture Layout With a Multimodal LLM
Wang, C., Zhong, H., Chai, M., He, M., Chen, D., & Liao, J. (2025) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2025.3626731 · Cited by 7 · see citing works
“LEGO-Net [43] proposes a data-driven, transformer-based iterative method for furniture rearrangement in a cluttered room.”
FreeScene: Mixed Graph Diffusion for 3D Scene Synthesis from Free Prompts
Bai, T., Bai, W., Chen, D., Wu, T., Li, M., & Ma, R. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.00553 · Cited by 5 · see citing works
“The architectures of deep generative models have ranged from convolutional networks [42, 52], recursive neural networks [14, 26], graph neural networks [53, 68], to Transformers [36] and diffusion models [49, 55].”
ArtiLatent: Realistic Articulated 3D Object Generation via Structured Latents
Chen, H., Lan, Y., Chen, Y., & Pan, X. (2025) · Proceedings of the SIGGRAPH Asia 2025 Conference Papers · doi:10.1145/3757377.3763922 · Cited by 4 · see citing works
“Structured generation has also been extended to scene composition [Tang et al. 2024; Wang et al. 2021; Wei et al. 2023] and architectural layout synthesis [Nauata et al. 2020, 2021; Shabani et al. 2023; Tang et al. 2023], where spatial and relational constraints are explicitly encoded.”
FurniScene: A Large-scale 3D Room Dataset with Intricate Furnishing Scenes
Wang, Y., Peng, J., Zhang, G., Luo, C., Xu, S., Zhang, M., & Zhang, Z. (2026) · International Journal of Computer Vision · doi:10.1007/s11263-025-02634-w · Cited by 4 · see citing works
“These methods can be roughly categorized three classes: forward neural generative models [9, 20, 25, 41, 42, 45], autoregressive methods [19, 23, 24, 28, 36–38], and meth-ods based on diffusion models [33, 39].”
Diorama: Unleashing Zero-Shot Single-View 3D Indoor Scene Modeling
Wu, Q., Iliash, D., Ritchie, D., Savva, M., & Chang, A. X. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00832 · Cited by 4 · see citing works
“Note the objects were already identified and provided as input.”
Chen, W., Xiao, C., Gao, G., Sun, F., Zhang, C., & Zhang, J. (2025) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2025.3611698 · Cited by 4 · see citing works
“Similar to [8], we employ positional encodings of certain frequencies and subsequent linear layers to convert t i , r i , and s i into vectors.”
InfluentialLLM-GROP: Visually grounded robot task and motion planning with large language models
Zhang, X., Ding, Y. F., Hayamizu, Y., Altaweel, Z., Zhu, Y., Zhu, Y., Stone, P., Paxton, C., & Zhang, S. (2025) · The International Journal of Robotics Research · doi:10.1177/02783649251378196 · Cited by 3 · see citing works
“In the past, researchers have equipped mobile manipulators with semantic information using machine learning methods (Liu et al., 2022b, 2022a; Wei et al., 2023; Zhang and Chai, 2021).”
Robot Active Task Cognition: Situation-Aware Task Planning With Large Language Models
Zhang, Y., Bian, S., Liu, J., Song, R., Zhu, D., Zhang, C., & Hua, C. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3637229 · Cited by 3 · see citing works
“Wu et al. [17] and Wei et al. [18] employed gradient fields for guidance.”
Positional diffusion: Graph-based diffusion models for set ordering
Giuliari, F., Scarpellini, G., Fiorini, S., James, S., Morerio, P., Wang, Y., & Bue, A. D. (2024) · Pattern Recognition Letters · doi:10.1016/j.patrec.2024.10.010 · Cited by 6 · see citing works
GLTScene: Global‐to‐Local Transformers for Indoor Scene Synthesis with General Room Boundaries
Li, Y., Xu, P., Ren, J., Shao, Z., & Huang, H. F. (2024) · Computer Graphics Forum · doi:10.1111/cgf.15236 · Cited by 3 · see citing works
Cho, Y., & Kim, J. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-20473-8 · Cited by 2 · see citing works
“In this regard, a network model has been proposed to rearrange objects regularly based on properties such as object size and shape [30, 40, 41].”
CasaGPT: Cuboid Arrangement and Scene Assembly for Interior Design
Feng, W., Zhou, H., Liao, J., Cheng, L., & Zhou, W. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.02716 · Cited by 2 · see citing works
“Representing indoor scenes as graphs has been widely explored in [8, 13, 21, 38, 40, 42, 51, 56].”
Structured-NeRF: Hierarchical Scene Graph with Neural Representation
Zhong, Z., Cao, J., Gu, S., Xie, S., Luo, L., Zhao, H., Zhou, G., Li, H., & Yan, Z. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72761-0_11 · Cited by 2 · see citing works
Online Diffusion-Based 3D Occupancy Prediction at the Frontier with Probabilistic Map Reconciliation
Reed, A., Achey, L., Crowe, B., Hayes, B., & Heckman, C. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11128832 · Cited by 2 · see citing works
“This biases the model to predict occupancy for these specific types of occlusions and does not scale well to large unseen sections of an environment.”
PACA: Perspective-Aware Cross-Attention Representation for Zero-Shot Scene Rearrangement
Jin, S., Wang, R., Chen, K., & Pokorny, F. T. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00639 · Cited by 1 · see citing works
“Most of these methods rely on extensive training on large-scale datasets and generate outputs based on the learned distributions [60].”
Text‐Guided Interactive Scene Synthesis with Scene Prior Guidance
Fang, S., Yang, H., Mooney, R. J., & Qi-xing Huang, A. (2025) · Computer Graphics Forum · doi:10.1111/cgf.70039 · Cited by 1 · see citing works
Cho, Y., Lee, J., Cho, S.-H., & Kim, J. (2025) · Journal of the Korea Computer Graphics Society · doi:10.15701/kcgs.2025.31.2.13 · Cited by 1 · see citing works
SceneFlow: Synthesizing indoor scenes via geometry-enhanced flow matching
Wu, W., Shen, A., Yin, Y., Chen, Z., Zhang, G., & Zheng, L. (2025) · Computer Aided Geometric Design · doi:10.1016/j.cagd.2025.102489 · Cited by 1 · see citing works
Programmable-Room: Interactive Textured 3D Room Meshes Generation Empowered by Large Language Models
Kim, J., Park, J., Kong, K., & Kang, S.-J. (2025) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2025.3581748 · Cited by 1 · see citing works
“Not to mention, editing the scene numerous times, as the perfectly satisfying room is often a result of refining the details over time.”
Self-citationGFPack++: Attention-Driven Gradient Fields for Optimizing 2D Irregular Packing
Xue, T., Lü, L., Liu, Y., Wu, M., Dong, H., Zhang, Y., Han, R., & Chen, B. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.01674 · Cited by 4 · see citing works
“Recent studies [19, 35, 36, 38] show that object rearrangement tasks can implicitly learn object arrangements from training set, enabling object movement without explicit goals.”
InteriorAgent: LLM Agent for Interior Design-Aware 3D Layout Generation
Gupta, K., Mehta, I., Wang, K., Chua, N., Krishna, A., Deng, Y., Ramamoorthi, R., & Chandraker, M. K. (2026) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv69130.2026.00138 · Cited by 1 · see citing works
“Learning-Based Methods Recent works have used Gaussian mixtures [7, 19], autoregressive transformers [13, 17, 22], recursive autoencoder [14], and graph convolution networks [36, 43] to learn indoor scene layouts, as well as diffusion models [34, 38, 40] for indoor scene synthesis.”
Build-It-Here: Utilizing 2D Inpainting Models for On-Site 3D Object Generation
Wu, Z., & Sato, H. (2024) · International Conference on Computational Intelligence and Applications (ICCIA) · doi:10.1109/iccia62557.2024.10719286 · Cited by 1 · see citing works
“Meanwhile, studies concentrating on structured scene generation [1] are generally limited to predefined room and furniture types, lacking the adaptability needed for customization.”
BackgroundLanPose: Language-Instructed 6D Object Pose Estimation for Robotic Assembly
Fu, B., Leong, S. K., Di, Y., Wang, G., Tang, J., Tombari, F., & Ji, X. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-91569-7_4
BuildingBlock: A Hybrid Approach for Structured Building Generation
Huang, J., Wang, C., Li, L. J., Huang, C., Dai, Q., & Xu, W. (2025) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/3721238.3730705
“In the content population stage, generated, retrieved, or predefined objects are populated with the layout, allowing for more customization while maintaining the overall structure.”
“Set it up”: Functional object arrangement with compositional generative models
Xu, Y., Mao, J., Li, L., Du, Y., Lozano‐Pérez, T., Kaelbling, L. P., & Hsu, D. (2025) · The International Journal of Robotics Research · doi:10.1177/02783649251378198
Creative Synthesis of Indoor Scene with Large Language Model
Lan, H., Cheng, Y., Gu, Y., He, C. Y., Cheng, X., & Lin, J. (2025) · Communications in computer and information science · doi:10.1007/978-981-95-4088-4_28
Mutual Space Representation Standardization for Mixed and Augmented Reality Remote Collaboration
Kim, S., & Woo, W. (2025) · IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct) · doi:10.1109/ismar-adjunct68609.2025.00101
Data-Bootstrapped, Physics-Informed Framework for Object Rearrangement
Wong, A., & Dong, Z. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11247659
“Current robotics research [4], [5], [6], [7], [8] often focuses on predicting final tidy states from untidy ones by learning from manually labeled tidy examples.”
Ko, D., Yeo, C., Kim, D., Kim, J., & Hwang, H. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3617738
“Research in virtual scene generation encompasses approaches focusing on scene synthesis [25], [26], often resulting in static environments that limit physical robot interaction.”
Space-Aware Indoor Furniture Recommendation and Placement using Agentic AI
Goswami, P., Rai, G., Akula, V. S. A., Sharma, O., & Imran, M. (2025) · IEEE International Conference on Intelligent Reality (ICIR) · doi:10.1109/icir68135.2025.11361589
“Forest2Seq [20] revitalizes object placement order using sequence modeling, while Lego-Net [21] learns to rearrange objects into regular, human-like configurations.”
Chen, Y., Duan, R., Jain, R., Hu, Y., Zhu, C., Shi, J., & Karthik Ramani, A. (2026) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3742413.3789142
“(3) Generated Image Li-brary displays generated images, allowing users to easily browse results, compare variations, and mark preferred outcomes.”
ScanEdit: Hierarchically-Guided Functional 3D Scan Editing
Boudjoghra, M. E. A., Ivan Laptev, A., & Dai, A. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.02516
“Recent methods such as LEGO-Net [50], Com-monScene [60], and DiffuScene [45] further improve scene plausibility and coherence.”
Embodied AI for kitchen scene rearrangement: a deep spatio-temporal approach
Esfahani, A. N., Hosseini, H. M., Masouleh, M. T., & Kalhor, A. (2026) · Intelligent Service Robotics · doi:10.1007/s11370-026-00723-4
Egocentric Perception for Open Vocabulary Object Rearrangement
Anand, M., Aggarwal, S., Anand, K., Omar, A., & Selventhiran, S. (2025) · Springer proceedings in mathematics & statistics · doi:10.1007/978-981-95-3841-6_10
Cho, Y., & Kim, J. (2026) · Journal of the Korea Computer Graphics Society · doi:10.15701/kcgs.2026.32.3.47
Survey on Compositional 3D Indoor Scene Generation
Tam, H. I. I., Pun, H. I. D., Wang, A. T., Sun, X., Wu, Q., Lee, H., Chang, A. X., & Savva, M. (2026) · Computer Graphics Forum · doi:10.1111/cgf.70591
OpenAsset: A Pipeline for Open-World Asset Integration into Indoor Scene Synthesis
Hong, Y., & Li, W. (2026) · Electronics · doi:10.3390/electronics15173943
Taming LLMs for Codematic Indoor Scene Generation
Liang, Y., Wu, Q., Fang, C., Chen, R., Liu, J., Zhang, J., & Tan, P. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37035-8_22
My-Meetings: LLM-Based Multi-agent Conference Scene Designer
Cheng, X., & Feng, G. (2026) · Communications in computer and information science · doi:10.1007/978-981-92-3991-7_16
3D Software Synthesis Driven by Constraint-Expressive Intermediate Representation
Li, S., Lam, A. Y., Peng, Y., Wang, W., & Lyu, M. R.-T. (2026) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3744916.3787812
“Then, deep learning meth-ods [43, 46, 66, 74, 80, 83, 87, 88, 92, 96] dominated.”
- Authors
- Wei, Q. A., Ding, S., Park, J. J., Sajnani, R., Poulenard, A., Sridhar, S., & Guibas, L.
- Affiliations
- Brown University (US)
- Stanford University (US)
- Venue
- 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2023-06
- Pages
- 19037-19047
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4386075912
- Cited by
- 60
- References
- 79
- Citations by year
- 2026: 10 · 2025: 29 · 2024: 16 · 2023: 5
- FWCI
- 12.74
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- AFOSR (FA9550-21-1-0214)
- Topics
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Processing and 3D Reconstruction
- Keywords
- symmetry, iterative denoising, transformer models
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