  Fully-supervised category-level pose estimation aims to determine the 6-DoF
poses of unseen instances from known categories, requiring expensive mannual
labeling costs. Recently, various self-supervised category-level pose
estimation methods have been proposed to reduce the requirement of the
annotated datasets. However, most methods rely on synthetic data or 3D CAD
model for self-supervised training, and they are typically limited to
addressing single-object pose problems without considering multi-objective
tasks or shape reconstruction. To overcome these challenges and limitations, we
introduce a diffusion-driven self-supervised network for multi-object shape
reconstruction and categorical pose estimation, only leveraging the shape
priors. Specifically, to capture the SE(3)-equivariant pose features and 3D
scale-invariant shape information, we present a Prior-Aware Pyramid 3D Point
Transformer in our network. This module adopts a point convolutional layer with
radial-kernels for pose-aware learning and a 3D scale-invariant graph
convolution layer for object-level shape representation, respectively.
Furthermore, we introduce a pretrain-to-refine self-supervised training
paradigm to train our network. It enables proposed network to capture the
associations between shape priors and observations, addressing the challenge of
intra-class shape variations by utilising the diffusion mechanism. Extensive
experiments conducted on four public datasets and a self-built dataset
demonstrate that our method significantly outperforms state-of-the-art
self-supervised category-level baselines and even surpasses some
fully-supervised instance-level and category-level methods.
