AniGrad: Anisotropic Gradient-Adaptive Sampling for 3D Reconstruction From Monocular Video
Noah Stier, Alex Rich, Pradeep Sen, Tobias Höllerer
摘要
Figure 1 . Reconstruction with adaptive & anisotropic resolution. Our 3D reconstruction system allocates computational resources effectively, using a variable sample pattern that adapts to the local surface complexity and orientation. A) The floor, which is flat and consistently oriented, is accurately reconstructed with a coarse resolution (4 cm). B) The cabinet is coarsely sampled in the vertical dimension because it exhibits no variation along that axis. Horizontally, however, it is sampled finely enough to accurately localize the surface. C) Object boundaries and other high-curvature structures are densely sampled to avoid aliasing, and to maintain high fidelity. The scene shown here is from ScanNet++ [39] .
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- ScanNet++: A High-Fidelity Dataset of 3D Indoor ScenesChandan Yeshwanth, Yueh-Cheng Liu, Matthias Nießner, Angela DaiICCV 2023 · 被引用 659 次
- TransformerFusion: Monocular RGB Scene Reconstruction using TransformersAljaz Bozic, Pablo R. Palafox, Justus Thies, Angela Dai 等NeurIPS 2021 · 被引用 185 次
- NeRFusion: Fusing Radiance Fields for Large-Scale Scene ReconstructionXiaoshuai Zhang, Sai Bi, Kalyan Sunkavalli, Hao Su 等CVPR 2022 · 被引用 94 次
- VolumeFusion: Deep Depth Fusion for 3D Scene ReconstructionJaesung Choe, Sunghoon Im, François Rameau, Minjun Kang 等ICCV 2021 · 被引用 83 次
- FineRecon: Depth-aware Feed-forward Network for Detailed 3D ReconstructionNoah Stier, Anurag Ranjan, Alex Colburn, Yajie Yan 等ICCV 2023 · 被引用 29 次
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