核心概念
Diffusion-driven self-supervised network for multi-object shape reconstruction and categorical pose estimation.
要約
The article introduces a diffusion-driven self-supervised network for multi-object shape reconstruction and categorical pose estimation. It addresses challenges in capturing SE(3)-equivariant pose features and 3D scale-invariant shape information. The Prior-Aware Pyramid 3D Point Transformer module is presented, along with a Pretrain-to-Refine Self-Supervised Training paradigm. Extensive experiments show the method outperforms state-of-the-art approaches.
統計
Recently, various self-supervised category-level pose estimation methods have been proposed.
Extensive experiments conducted on four public datasets demonstrate the method significantly outperforms state-of-the-art baselines.
The project page is released at Self-SRPE.
引用
"Noise points are passed through the reverse Markov chain to form complete sharp shapes."
"Our proposed tasks aim to estimate 6-DoF poses and 3D shapes of multiple surrounding instances in the observed scene."