OP-Align: Object-level and Part-level Alignment for Self-supervised Category-level Articulated Object Pose Estimation

TL;DR

This paper introduces OP-Align, a self-supervised method for category-level articulated object pose estimation using single-frame point clouds, achieving high accuracy without extensive annotations.

Contribution

It presents a novel self-supervised framework that estimates object and part-level poses from a single point cloud, outperforming previous methods and including a new real-world benchmark dataset.

Findings

Outperforms previous self-supervised methods

Comparable to state-of-the-art supervised methods

Introduces a new real-world articulated object dataset

Abstract

Category-level articulated object pose estimation focuses on the pose estimation of unknown articulated objects within known categories. Despite its significance, this task remains challenging due to the varying shapes and poses of objects, expensive dataset annotation costs, and complex real-world environments. In this paper, we propose a novel self-supervised approach that leverages a single-frame point cloud to solve this task. Our model consistently generates reconstruction with a canonical pose and joint state for the entire input object, and it estimates object-level poses that reduce overall pose variance and part-level poses that align each part of the input with its corresponding part of the reconstruction. Experimental results demonstrate that our approach significantly outperforms previous self-supervised methods and is comparable to the state-of-the-art supervised methods. To assess the performance of our model in real-world scenarios, we also introduce a new real-world articulated object benchmark dataset.