Reasoning and Learning a Perceptual Metric for Self-Training of Reflective Objects in Bin-Picking with a Low-cost Camera

• URL: http://arxiv.org/abs/2503.20207v1
• Date: Wed, 26 Mar 2025 04:03:51 GMT
• Title: Reasoning and Learning a Perceptual Metric for Self-Training of Reflective Objects in Bin-Picking with a Low-cost Camera
• Authors: Peiyuan Ni, Chee Meng Chew, Marcelo H. Ang Jr., Gregory S. Chirikjian,
• Abstract summary: Bin-picking of metal objects using low-cost RGB-D cameras often suffers from sparse depth information and reflective surface textures.<n>We propose a two-stage framework consisting of a metric learning stage and a self-training stage.<n>Our approach outperforms several state-of-the-art methods on both the ROBI dataset and our newly introduced Self-ROBI dataset.
• Score: 10.976379239028455
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Bin-picking of metal objects using low-cost RGB-D cameras often suffers from sparse depth information and reflective surface textures, leading to errors and the need for manual labeling. To reduce human intervention, we propose a two-stage framework consisting of a metric learning stage and a self-training stage. Specifically, to automatically process data captured by a low-cost camera (LC), we introduce a Multi-object Pose Reasoning (MoPR) algorithm that optimizes pose hypotheses under depth, collision, and boundary constraints. To further refine pose candidates, we adopt a Symmetry-aware Lie-group based Bayesian Gaussian Mixture Model (SaL-BGMM), integrated with the Expectation-Maximization (EM) algorithm, for symmetry-aware filtering. Additionally, we propose a Weighted Ranking Information Noise Contrastive Estimation (WR-InfoNCE) loss to enable the LC to learn a perceptual metric from reconstructed data, supporting self-training on untrained or even unseen objects. Experimental results show that our approach outperforms several state-of-the-art methods on both the ROBI dataset and our newly introduced Self-ROBI dataset.

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