用低成本相机自动识别金属物体，减少人工标注
Reasoning and Learning a Perceptual Metric for Self-Training of Reflective Objects in Bin-Picking with a Low-cost Camera
- 通过推理约束优化物体姿态猜测
- 自训练使模型能处理未见金属物
- 适合工业自动化场景应用
使用低成本RGB-D相机进行金属物体抓取时，常因深度信息稀疏和反光表面导致误检，需人工标注。为此提出两阶段框架：第一阶段采用多物体姿态推理（MoPR）算法，在深度、碰撞和边界约束下优化姿态假设；第二阶段引入对称性感知的李群贝叶斯高斯混合模型（SaL-BGMM）结合期望最大化（EM）算法，实现对姿态候选的对称性过滤。此外，设计加权排名信息噪声对比损失（WR-InfoNCE），使低成本相机从重构数据中学习感知度量，支持对未训练或未见过物体的自训练。实验表明，该方法在ROBI数据集及新构建的Self-ROBI数据集上均优于多个前沿方法。
原文摘要 · Abstract (English)
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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