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Coordinate-based implicit neural networks, or neural fields, have emerged asuseful representations of shape and appearance in 3D computer vision. Despiteadvances, however, it remains challenging to build neural fields for categoriesof objects without datasets like ShapeNet that provide "canonicalized" objectinstances that are consistently aligned for their 3D position and orientation(pose). We present Canonical Field Network (CaFi-Net), a self-supervised methodto canonicalize the 3D pose of instances from an object category represented asneural fields, specifically neural radiance fields (NeRFs). CaFi-Net directlylearns from continuous and noisy radiance fields using a Siamese networkarchitecture that is designed to extract equivariant field features forcategory-level canonicalization. During inference, our method takes pre-trainedneural radiance fields of novel object instances at arbitrary 3D pose andestimates a canonical field with consistent 3D pose across the entire category.Extensive experiments on a new dataset of 1300 NeRF models across 13 objectcategories show that our method matches or exceeds the performance of 3D pointcloud-based methods.

Canonical Fields: Self-Supervised Learning of Pose-Canonicalized Neural Fields