GP-NeRF: Generalized Perception NeRF for Context-Aware 3D Scene Understanding
Hao Li, Dingwen Zhang, Yalun Dai, Nian Liu, Lechao Cheng, Jingfeng Li, Jingdong Wang, Junwei Han
摘要
Figure 1. Our method, called GP-NeRF, achieves remarkable performance improvements for instance and semantic segmentation in both synthesis [35] and real-world [10] datasets, as shown in the right column of the figure. Here we showcase generalized semantic segmentation, finetuning semantic segmentation, and instance segmentation) with their corresponding reconstruction results. For the left column, the qualitative results of the visualization are presented, showing the effectiveness of our method for simultaneous segmentation and reconstruction. What's more, we visualize our rendered features via PCA in the novel view, demonstrating our method possesses the capability to produce semantic-aware features that can distinguish between different classes and objects.
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引用它的顶会 Paper7
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它引用的顶会 Paper32
- Learning Transferable Visual Models From Natural Language SupervisionAlec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh 等ICML 2021 · 被引用 47,906 次
- Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance FieldsJonathan T. Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman 等ICCV 2021 · 被引用 2,700 次
- Per-Pixel Classification is Not All You Need for Semantic SegmentationBowen Cheng, Alexander G. Schwing, Alexander KirillovNeurIPS 2021 · 被引用 2,196 次
- Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance FieldsJonathan T. Barron, Ben Mildenhall, Dor Verbin, Pratul P. Srinivasan 等CVPR 2022 · 被引用 1,603 次
- Nerfies: Deformable Neural Radiance FieldsKeunhong Park, Utkarsh Sinha, Jonathan T. Barron, Sofien Bouaziz 等ICCV 2021 · 被引用 1,442 次
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