SUN RGB-D: A RGB-D scene understanding benchmark suite

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155

ImageNet: A large-scale hierarchical image database

Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333

Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684

The pascal visual object classes (VOC) challenge

Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599

Object Detection with Discriminatively Trained Part-Based Models

Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.167 · Cited by 9,853

Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope

Oliva, A., & Torralba, A. B. (2001) · International Journal of Computer Vision · doi:10.1023/a:1011139631724 · Cited by 6,321

Indoor segmentation and support inference from rgbd images

Silberman, N., Hoiem, D., Kohli, P., & Fergus, R. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33715-4_54 · Cited by 5,760

3D ShapeNets: A deep representation for volumetric shapes

Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., & Xiao, J. (2015) · 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.2015.7298801 · Cited by 5,413

A benchmark for the evaluation of RGB-D SLAM systems

Sturm, J., Engelhard, N., Endres, F., Burgard, W., & Cremers, D. (2012) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2012.6385773 · Cited by 4,230

LabelMe: A Database and Web-Based Tool for Image Annotation

Russell, B. C., Torralba, A., Murphy, K. P., & Freeman, W. T. (2008) · International Journal of Computer Vision · doi:10.1007/s11263-007-0090-8 · Cited by 4,210

Advancements in deep learning for point cloud classification and segmentation: A comprehensive review

Zhou, W. M., Liu, K., Jin, W., Wang, Q., She, Y., Yu, Y., & Ma, C. (2025) · Computers & Graphics · doi:10.1016/j.cag.2025.104238 · Cited by 15

Completion as Enhancement: A Degradation-Aware Selective Image Guided Network for Depth Completion

Yan, Z., Wang, Z., Wang, K., Li, J., & Yang, J. Y. (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/cvpr52734.2025.02509 · Cited by 12

Segmentation of RGB-D indoor scenes by stacking random forests and conditional random fields

Thøgersen, M. B., Escalera, S., Gonzàlez, J., & Moeslund, T. B. (2016) · Pattern Recognition Letters · doi:10.1016/j.patrec.2016.06.024 · Cited by 12

Multi-View 3D Entangled Forest for Semantic Segmentation and Mapping

Antonello, M., Wolf, D., Prankl, J., Ghidoni, S., Menegatti, E., & Vincze, M. (2018) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2018.8460837 · Cited by 12

Spatial Geometric Reasoning for Room Layout Estimation via Deep Reinforcement Learning

Ren, L., Song, Y., Lu, J., & Zhou, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58583-9_33 · Cited by 12

A Deep Learning-based Indoor Scene Classification Approach Enhanced with Inter-Object Distance Semantic Features

Pereira, R., Garrote, L., Barros, T., Lopes, A., & Nunes, U. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636242 · Cited by 12

Depth Removal Distillation for RGB-D Semantic Segmentation

Fang, T., Liang, Z., Shao, X., Dong, Z., & Li, J. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747767 · Cited by 12

Multi-Sensor Large-Scale Dataset for Multi-View 3D Reconstruction

Voynov, O., Bobrovskikh, G., Karpyshev, P., Galochkin, S., Ardelean, A.-T., Bozhcnko, A., Karmanova, E., Kopanev, P., Labutin-Rymsho, Y., Rakhimov, R., et al. (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/cvpr52729.2023.02049 · Cited by 12

Three-dimensional reconstruction using SFM for actual pedestrian classification

Gomez‐Donoso, F., Castaño-Amorós, J., Escalona, F., & Cazorla, M. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119006 · Cited by 12

3D Change Localization and Captioning from Dynamic Scans of Indoor Scenes

Qiu, Y., Yamamoto, S., Yamada, R., Suzuki, R., Kataoka, H., Iwata, K., & Satoh, Y. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00123 · Cited by 12

Class-Level Confidence Based 3D Semi-Supervised Learning

Chen, Z., Jing, L., Yang, L., Li, Y., & Li, B. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00070 · Cited by 12

A LiDAR Multi-Object Detection Algorithm for Autonomous Driving

Wang, S., & Chen, M. (2023) · Applied Sciences · doi:10.3390/app132312747 · Cited by 12

Leveraging RGB-D Data with Cross-Modal Context Mining for Glass Surface Detection

Lin, J., Yeung, Y.-H., Ye, S., & Lau, R. W. H. (2025) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v39i5.32558 · Cited by 12

Multi-class indoor semantic segmentation with deep structured model

Zheng, C., Wang, J., Chen, W., & Wu, X. (2017) · The Visual Computer · doi:10.1007/s00371-017-1411-8 · Cited by 12

Learning deep representations for semantic image parsing: a comprehensive overview

Huang, L., Peng, J., Zhang, R., Li, G., & Lin, L. (2018) · Frontiers of Computer Science · doi:10.1007/s11704-018-7195-8 · Cited by 12

Clouds of Oriented Gradients for 3D Detection of Objects, Surfaces, and Indoor Scene Layouts

Ren, Z., & Sudderth, E. B. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2923201 · Cited by 12

Indoor scene understanding via RGB-D image segmentation employing depth-based CNN and CRFs

Li, W., Gu, J., Dong, Y., Dong, Y., & Han, J. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-07882-w · Cited by 14

Robust depth map inpainting using superpixels and non-local Gauss–Markov random field prior

Satapathy, S., & Sahay, R. R. (2021) · Signal Processing Image Communication · doi:10.1016/j.image.2021.116378 · Cited by 12

Multi-domain semantic segmentation with overlapping labels

Bevandić, P., Oršić, M., Grubišić, I., Šarić, J., & Šegvić, S. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00248 · Cited by 15

SemanticPOSS: A Point Cloud Dataset with Large Quantity of Dynamic InstancesPreprint

Pan, Y., Gao, B., Mei, J., Geng, S., Li, C., & Zhao, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.09147 · Cited by 12

Global-Local Propagation Network for RGB-D Semantic SegmentationPreprint

Chen, S., Zhu, X., Liu, W., He, X., & Liu, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.10801 · Cited by 12

Procedural Generation of Narrative Worlds

Balint, J. T., & Bidarra, R. (2023) · IEEE Transactions on Games · doi:10.1109/tg.2022.3216582 · Cited by 11

Robust Multi-View Clustering Through Partition Integration on Stiefel Manifold

Hu, Y., Guo, E., Xie, Z., Liu, X., & Cai, H. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3253244 · Cited by 11

Deep Monocular Depth Estimation Based on Content and Contextual Features

Abdulwahab, S., Rashwan, H. A., Sharaf, N., Khalid, S., & Puig, D. (2023) · Sensors · doi:10.3390/s23062919 · Cited by 11

Point-NAS: A Novel Neural Architecture Search Framework for Point Cloud Analysis

Xie, T., Zhang, H., Yang, L., Wang, K., Dai, K., Li, R., & Zhao, L. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3331223 · Cited by 11

Semantic Consistency Reasoning for 3-D Object Detection in Point Clouds

Wei, W., Wei, P., Liao, Z., Qin, J., Cheng, X., Liu, M., & Zheng, N. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3341097 · Cited by 11

3DRef: 3D Dataset and Benchmark for Reflection Detection in RGB and Lidar Data

Zhao, X., & Schwertfeger, S. (2024) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv62453.2024.00009 · Cited by 11

The visual experience dataset: Over 200 recorded hours of integrated eye movement, odometry, and egocentric video

Greene, M. R., Balas, B., Lescroart, M. D., MacNeilage, P. R., Hart, J. A., Binaee, K., Hausamann, P., Mezile, R., Shankar, B., Sinnott, C., et al. (2024) · Journal of Vision · doi:10.1167/jov.24.11.6 · Cited by 16

Diff3DETR: Agent-Based Diffusion Model for Semi-supervised 3D Object Detection

Deng, J., Lu, J., & Zhang, T. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72754-2_4 · Cited by 14

OmniNOCS: A Unified NOCS Dataset and Model for 3D Lifting of 2D Objects

Krishnan, A., Kundu, A., Maninis, K.-K., Hays, J. H., & Brown, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73226-3_8 · Cited by 11

Riemannian Optimization for Active Mapping With Robot Teams

Asgharivaskasi, A., Girke, F., & Atanasov, N. (2025) · IEEE Transactions on Robotics · doi:10.1109/tro.2025.3526295 · Cited by 11

LCASAFormer: Cross-attention enhanced backbone network for 3D point cloud tasks

Guo, S., Cai, J., Hu, Y., Liu, Q., & Xu, M. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.111361 · Cited by 11

Dynamic Learnable Label Assignment for Indoor 3D Object Detection

Liu, X., Zhao, L., Fan, B., Lu, J., & Liu, H. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3563083 · Cited by 11

3D Reconstruction in Robotics: A Comprehensive Review

Selvaratnam, D., & Bazazian, D. (2025) · Computers & Graphics · doi:10.1016/j.cag.2025.104256 · Cited by 11

Review of deep learning-based segmentation methods: Popular approaches, literature gaps, and opportunities

ÇELİK, M., & İni̇k, Ö. (2025) · Displays · doi:10.1016/j.displa.2025.103225 · Cited by 15

Depth Recovery for Kinect Sensor Using Contour-Guided Adaptive Morphology Filter

Ti, C., Xu, G., Guan, Y., & Teng, Y. (2017) · IEEE Sensors Journal · doi:10.1109/jsen.2017.2707522 · Cited by 11

Transfer Vision Patterns for Multi-Task Pixel Learning

Zhang, X., Zhou, L., Li, Y., Cui, Z., Xie, J., & Yang, J. J. (2021) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3474085.3475501 · Cited by 11

Visual Micro-Pattern Propagation

Cui, Z., Zhou, L., Wang, C., Xu, C., & Yang, J. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3147974 · Cited by 11

Knowledge driven indoor object‐goal navigation aid for visually impaired people

Hou, X., Zhao, H., Wang, C., & Liu, H. (2022) · Cognitive Computation and Systems · doi:10.1049/ccs2.12061 · Cited by 11

PointSee: Image Enhances Point Cloud

Gu, L., Yan, X., Cui, P., Gong, L., Xie, H., Wang, F. L., Qin, J., & Wei, M. (2023) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2023.3331779 · Cited by 11

End-to-end learning for joint depth and image reconstruction from diffracted rotation

Mel, M., Siddiqui, M., & Zanuttigh, P. (2023) · The Visual Computer · doi:10.1007/s00371-023-03147-8 · Cited by 15

CO-Net: Learning Multiple Point Cloud Tasks at Once with A Cohesive Network

Xie, T., Wang, K., Lu, S., Zhang, Y., Dai, K., Li, X., Xu, J., Wang, K., Zhao, L., Zhang, X., et al. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00326 · Cited by 11

Dual-Perspective Knowledge Enrichment for Semi-supervised 3D Object Detection

Han, Y., Zhao, N., Chen, W., Ma, K. T., & Zhang, H. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i3.27976 · Cited by 11

Towards open-set semantic labeling in 3D point clouds : Analysis on the unknown class

Ma, H., Xiong, R., Wang, Y., Kodagoda, S., & Shi, L. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.09.071 · Cited by 11

Automatic Spatially Varying Illumination Recovery of Indoor Scenes Based on a Single RGB-D Image

Xing, G., Liu, Y., Ling, H., Granier, X., & Zhang, Y. (2018) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2018.2876541 · Cited by 11

Casting Geometric Constraints in Semantic Segmentation as Semi-Supervised Learning

Stekovic, S., Fraundorfer, F., & Lepetit, V. (2020) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv45572.2020.9093571 · Cited by 11

A generalized semantic representation for procedural generation of rooms

Balint, J. T., & Rafael Bidarra, E. (2019) · International Conference on the Foundations of Digital Games (FDG) · doi:10.1145/3337722.3341848 · Cited by 11

Single Image 3D Vehicle Pose Estimation for Augmented Reality

Lu, Y., Kourian, S., Salvaggio, C., Xu, C., & Lu, G. (2019) · IEEE Global Conference on Signal and Information Processing · doi:10.1109/globalsip45357.2019.8969201 · Cited by 11

Survey on Scene Classification techniques

Patel, T., Dabhi, V. K., & Prajapati, H. B. (2020) · International Conference on Advanced Computing and Communication Systems (ICACCS) · doi:10.1109/icaccs48705.2020.9074460 · Cited by 11

Acoustic Room Modelling Using 360 Stereo Cameras

Kim, H., Remaggi, L., Fowler, S., Jackson, P. J. B., & Hilton, A. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2020.3037537 · Cited by 11

SPOT: Selective Point Cloud Voting for Better Proposal in Point Cloud Object Detection

Du, H., Li, L., Liu, B., & Vasconcelos, N. M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58621-8_14 · Cited by 11

Time- and Resource-Efficient Time-to-Collision Forecasting for Indoor Pedestrian Obstacles Avoidance

Urban, D., & Caplier, A. (2021) · Journal of Imaging · doi:10.3390/jimaging7040061 · Cited by 11

Learning deep cross-scale feature propagation for indoor semantic segmentation

Huan, L., Zheng, X., Tang, S., & Gong, J. (2021) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2021.03.023 · Cited by 11

MBDF-Net

Tan, X., Chen, X., Zhang, G., Ding, J., & Lan, X. (2021) · doi:10.1145/3475721.3484311 · Cited by 11

CDSFusion: Dense Semantic SLAM for Indoor Environment Using CPU Computing

Wang, S., Gou, G., Sui, H., Zhou, Y., Zhang, H., & Li, J. (2022) · Remote Sensing · doi:10.3390/rs14040979 · Cited by 11

Semantic mapping and semantics-boosted navigation with path creation on a mobile robot

Sun, H., Meng, Z., & Ang, M. H. (2017) · IEEE Conference on Cybernetics and Intelligent Systems · doi:10.1109/iccis.2017.8274775 · Cited by 11

Fukuoka datasets for place categorization

Mozos, Ó. M., Nakashima, K., Jung, H., Iwashita, Y., & Kurazume, R. (2019) · The International Journal of Robotics Research · doi:10.1177/0278364919835603 · Cited by 11

The 3D-Pitoti Dataset

Poier, G., Seidl, M., Zeppelzauer, M., Reinbacher, C., Schaich, M., Bellandi, G., Marretta, A., & Bischof, H. (2017) · International Workshop on Content-Based Multimedia Indexing (CBMI) · doi:10.1145/3095713.3095719 · Cited by 17

OpenRooms: An End-to-End Open Framework for Photorealistic Indoor Scene DatasetsPreprint

Li, Z., Yu, T.-W., Sang, S., Wang, S., Song, M., Liu, Y., Yeh, Y.-Y., Zhu, R., Gundavarapu, N., Shi, J., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.12868 · Cited by 11

Cross-Domain Facial Expression Recognition: A Unified Evaluation Benchmark and Adversarial Graph LearningPreprint

Chen, T., Pu, T., Wu, H., Xie, Y., Liu, L., & Lin, L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.00923 · Cited by 11

RGB-D indoor semantic segmentation network based on wavelet transform

Fan, R., Liu, Y., Jiang, S., & Zhang, R. (2022) · Evolving Systems · doi:10.1007/s12530-022-09479-5 · Cited by 10

Part-Guided 3D RL for Sim2Real Articulated Object Manipulation

Xie, P., Chen, R., Chen, S., Qin, Y., Xiang, F., Sun, T., Xu, J., Wang, G., & Su, H. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3313063 · Cited by 10

CUS3D: A New Comprehensive Urban-Scale Semantic-Segmentation 3D Benchmark Dataset

Gao, L., Liu, Y., Chen, X., Liu, Y., Yan, S., & Zhang, M. (2024) · Remote Sensing · doi:10.3390/rs16061079 · Cited by 10

LUTein: Dense-Sparse Bit-Slice Architecture With Radix-4 LUT-Based Slice-Tensor Processing Units

Im, D., & Yoo, H. (2024) · IEEE International Symposium on High-Performance Computer Architecture (HPCA) · doi:10.1109/hpca57654.2024.00063 · Cited by 11

Semantic Guidance Fusion Network for Cross-Modal Semantic Segmentation

Zhang, P., Chen, M., & Gao, M. (2024) · Sensors · doi:10.3390/s24082473 · Cited by 10

AGWNet: Attention-guided adaptive shuffle channel gate warped feature network for indoor scene RGB-D semantic segmentation

Xiong, B., Peng, Y., Zhu, J., Gu, J., Chen, Z., & Qin, W. (2024) · Displays · doi:10.1016/j.displa.2024.102730 · Cited by 10

A Survey on Non-Photorealistic Rendering Approaches for Point cloud Visualization

Wegen, O., Scheibel, W., Trapp, M., Richter, R., & Döllner, J. (2024) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2024.3402610 · Cited by 10

MIPANet: optimizing RGB-D semantic segmentation through multi-modal interaction and pooling attention

Zhang, S., & Xie, M. (2024) · Frontiers in Physics · doi:10.3389/fphy.2024.1411559 · Cited by 10

BEHAVIOR Vision Suite: Customizable Dataset Generation via Simulation

Ge, Y., Tang, Y., Xu, J., Gökmen, C., Li, C., Ai, W., Martinez, B., Aydin, A., Anvari, M., Chakravarthy, A. K., et al. (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/cvpr52733.2024.02114 · Cited by 11

GeoAuxNet: Towards Universal 3D Representation Learning for Multi-Sensor Point Clouds

Zhang, S., Xin, F., & Duan, Y. (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/cvpr52733.2024.01892 · Cited by 10

RGB-D Cube R-CNN: 3D Object Detection with Selective Modality Dropout

Piekenbrinck, J., Hermans, A., Vaškevičius, N., Linder, T., & Leibe, B. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00205 · Cited by 10

3D Lidar Point Cloud Segmentation for Automated Driving

Abbasi, R., Bashir, A. K., Rehman, A. U., & Ge, Y. (2024) · IEEE Intelligent Transportation Systems Magazine · doi:10.1109/mits.2023.3325854 · Cited by 10

Leaf Disease Segmentation Using Uunet++ Architecture

Farooqui, N. A., Mishra, A. K., Ray, K., & Mallik, S. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-3937-0_52 · Cited by 10

Depth-Guided Texture Diffusion for Image Semantic Segmentation

Sun, W., Li, Y., Ye, Q., Jiao, J., & Zhou, Y. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3484490 · Cited by 12

LVP: Leverage Virtual Points in Multimodal Early Fusion for 3-D Object Detection

Chen, Y., Cai, G., Song, Z., Liu, Z., Zeng, B., Li, J., & Wang, Z. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3519386 · Cited by 10

Enhancing RGB-D Mirror Segmentation With a Neighborhood-Matching and Demand-Modal Adaptive Network Using Knowledge Distillation

Zhou, W., Zhang, H., Liu, Y., & Luo, T. (2025) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2025.3547613 · Cited by 10

Cubify Anything: Scaling Indoor 3D Object Detection

Lazarow, J., Griffiths, D., Kohavi, G., Crespo, F., & Dehghan, 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/cvpr52734.2025.02070 · Cited by 10

UniAlign: Scaling Multimodal Alignment within One Unified Model

Zhou, B., Li, L., Wang, Y., Liu, H., Yao, Y., & Wang, 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/cvpr52734.2025.02760 · Cited by 13

Learning Multiviewpoint Context-Aware Representation for RGB-D Scene Classification

Zheng, Y., Ye, H., Wang, L., & Pu, J. (2017) · IEEE Signal Processing Letters · doi:10.1109/lsp.2017.2764489 · Cited by 10

Structure-Aware Multimodal Feature Fusion for RGB-D Scene Classification and Beyond

Wang, A., Cai, J., Lu, J., & Cham, T. (2018) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3115932 · Cited by 10

Tube-Embedded Transformer for Pixel Prediction

Zhang, X., Zhang, S., Cui, Z., Li, Z., Xie, J., & Yang, J. J. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3147664 · Cited by 10

Classification of single-view object point clouds

Xu, Z., Liu, K., Chen, K., Ding, C., Wang, Y., & Jia, K. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.109137 · Cited by 10

BCAF-3D: Bilateral Content Awareness Fusion for cross-modal 3D object detection

Chen, M., Liu, P., & Zhao, H. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110952 · Cited by 11

Not Every Side Is Equal: Localization Uncertainty Estimation for Semi-Supervised 3D Object Detection

Wang, C., Yang, W., & Zhang, T. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00353 · Cited by 10

PanoGlassNet: Glass Detection With Panoramic RGB and Intensity Images

Chang, Q., Liao, H., Meng, X., Xu, S., & Cui, Y. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3390163 · Cited by 10

Context-Assisted 3D (C3D) Object Detection from RGB-D Images

Ren, Y., Chen, C., Li, S., & Kuo, C. ‐. J. (2018) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2018.05.019 · Cited by 10

Spatial Aggregation Net: Point Cloud Semantic Segmentation Based on Multi-Directional Convolution

Cai, G., Jiang, Z., Wang, Z., Huang, S., Chen, K., Ge, X., & Wu, Y. (2019) · Sensors · doi:10.3390/s19194329 · Cited by 11

Finding Your (3D) Center: 3D Object Detection Using a Learned Loss

Griffiths, D., Boehm, J., & Ritschel, T. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58523-5_5 · Cited by 10

Par3DNet: Using 3DCNNs for Object Recognition on Tridimensional Partial Views

Gomez‐Donoso, F., Escalona, F., & Cazorla, M. (2020) · Applied Sciences · doi:10.3390/app10103409 · Cited by 10

Decomposition and replacement: Spatial knowledge distillation for monocular depth estimation

Song, M., & Kim, W. (2022) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2022.103523 · Cited by 11

Edge preserving range image smoothing using hybrid locally kernel-based weighted least square

Bahraini, T., Hamedani, T., Hosseini, S. M., & Yazdi, H. S. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.109234 · Cited by 10

A systematic review on open-set segmentation

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