Locate n' Rotate: Two-stage Openable Part Detection with Foundation Model Priors

TL;DR

This paper introduces a two-stage Transformer-based framework called MOPD for detecting openable parts of articulated objects, leveraging perceptual and geometric priors to improve generalization and accuracy in robotic applications.

Contribution

The paper presents a novel two-stage detection framework that incorporates perceptual grouping and geometric priors, enhancing openable part detection and motion prediction beyond existing methods.

Findings

Outperforms previous methods in detection accuracy

Improves motion parameter prediction accuracy

Demonstrates better generalization to unseen objects

Abstract

Detecting the openable parts of articulated objects is crucial for downstream applications in intelligent robotics, such as pulling a drawer. This task poses a multitasking challenge due to the necessity of understanding object categories and motion. Most existing methods are either category-specific or trained on specific datasets, lacking generalization to unseen environments and objects. In this paper, we propose a Transformer-based Openable Part Detection (OPD) framework named Multi-feature Openable Part Detection (MOPD) that incorporates perceptual grouping and geometric priors, outperforming previous methods in performance. In the first stage of the framework, we introduce a perceptual grouping feature model that provides perceptual grouping feature priors for openable part detection, enhancing detection results through a cross-attention mechanism. In the second stage, a geometric understanding feature model offers geometric feature priors for predicting motion parameters. Compared to existing methods, our proposed approach shows better performance in both detection and motion parameter prediction. Codes and models are publicly available at https://github.com/lisiqi-zju/MOPD