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