The DOI matches its Crossref record; 73 of the 73 references it cites were found in a registry or an index.
Mesh R-CNN
Abstract
Rapid advances in 2D perception have led to systems that accurately detect objects in real-world images. However, these systems make predictions in 2D, ignoring the 3D structure of the world. Concurrently, advances in 3D shape prediction have mostly focused on synthetic benchmarks and isolated objects. We unify advances in these two areas. We propose a system that detects objects in real-world images and produces a triangle mesh giving the full 3D shape of each detected object. Our system, called Mesh R-CNN, augments Mask R-CNN with a mesh prediction branch that outputs meshes with varying topological structure by first predicting coarse voxel representations which are converted to meshes and refined with a graph convolution network operating over the mesh's vertices and edges. We validate our mesh prediction branch on ShapeNet, where we outperform prior work on single-image shape prediction. We then deploy our full Mesh R-CNN system on Pix3D, where we jointly detect objects and predict their 3D shapes.
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Cites
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
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Bottleneck Transformers for Visual Recognition
Srinivas, A., Lin, T.-Y., Parmar, N., Shlens, J., Abbeel, P., & Vaswani, A. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01625 · Cited by 1,385
Convolutional Occupancy Networks
Peng, S., Niemeyer, M., Mescheder, L., Pollefeys, M., & Geiger, A. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58580-8_31 · Cited by 801
Artificial intelligence in the creative industries: a review
Anantrasirichai, N., & Bull, D. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-021-10039-7 · Cited by 793
Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstruction
Chabra, R., Lenssen, J. E., Ilg, E., Schmidt, T., Straub, J., Lovegrove, S., & Newcombe, R. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58526-6_36 · Cited by 380
Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding
Roberts, M., Ramapuram, J., Ranjan, A., Kumar, A., Bautista, M. A., Paczan, N., Webb, R., & Susskind, J. M. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01073 · Cited by 358
Metadata
- Authors
- Gkioxari, G., Johnson, J., & Malik, J.
- Venue
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2019-10
- Pages
- 9784-9794
- OpenAlex
- W2990578762
- Cited by
- 441
- References
- 73
- FWCI
- 74.27
- Type
- conference paper
- Language
- en
- Open access
- closed
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