Canonical Fields: Self-Supervised Learning of Pose-Canonicalized Neural Fields
Rohith Agaram, Shaurya Dewan, Rahul Sajnani, Adrien Poulenard, K. Madhava Krishna, Srinath Sridhar
Abstract
cs.brown.edu/projects/canonicalfields Figure 1 . We present Canonical Field Network (CaFi-Net), a self-supervised method for 3D position and orientation (pose) canonicalization of objects represented as neural fields. We specifically focus on neural radiance fields (NeRFs) fitted to raw RGB images of arbitrarily posed objects (left), and obtain a canonical field (fixed novel view shown on right) where all objects in a category are consistently positioned and oriented.
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Install the CLIlune papers fulltext a35ce8c2-a0a9-455f-8e6f-2bdb3d4a4100Cited by top-tier papers5
- A Canonicalization Perspective on Invariant and Equivariant LearningGeorge Ma, Yifei Wang, Derek Lim, Stefanie Jegelka et al.NeurIPS 2024 · 38 citations
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- One-shot 3D Object Canonicalization based on Geometric and Semantic ConsistencyLi Jin, Yujie Wang, Wenzheng Chen, Qiyu Dai et al.CVPR 2025
- FedMeNF: Privacy-Preserving Federated Meta-Learning for Neural FieldsJunhyeog Yun, Minui Hong, Gunhee KimICCV 2025
Builds on26
- Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance FieldsJonathan T. Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman et al.ICCV 2021 · 2,700 citations
- NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view ReconstructionPeng Wang, Lingjie Liu, Yuan Liu, Christian Theobalt et al.NeurIPS 2021 · 2,500 citations
- Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance FieldsJonathan T. Barron, Ben Mildenhall, Dor Verbin, Pratul P. Srinivasan et al.CVPR 2022 · 1,603 citations
- Nerfies: Deformable Neural Radiance FieldsKeunhong Park, Utkarsh Sinha, Jonathan T. Barron, Sofien Bouaziz et al.ICCV 2021 · 1,442 citations
- Volume Rendering of Neural Implicit SurfacesLior Yariv, Jiatao Gu, Yoni Kasten, Yaron LipmanNeurIPS 2021 · 1,421 citations
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