用几何先验提升非朗伯物体的深度可靠性，让机器人抓取更稳定
AISPO: Enhancing Depth Reliability for Robotic Manipulation of Non-Lambertian Objects via Affine-Invariant Shape Prior
- 融合多尺度图像与深度特征，引入仿射不变形状先验
- 在透明和高反光物体上显著减少深度缺失，提升抓取成功率
- 适合需要高精度深度感知的机器人抓取任务
可靠的深度感知对机器人操作至关重要，尤其在透明或高度镜面物体等非朗伯表面场景中，原始深度数据常被污染或缺失。此类问题会传递至运动规划，导致无效抓取姿态和执行失败。本文提出AISPO，一种深度补全框架，通过结合多尺度RGB-D特征融合与仿射不变形状先验，增强预测深度图的几何一致性，缓解灾难性深度失效。与侧重平均精度的方法不同，本方法强调物理合理性与结构完整性。大量基准测试表明其性能优异且泛化能力强，可适应未见过的物体与新场景。真实世界抓取实验进一步验证：深度可靠性提升显著改善了操作成功率，尤其在透明物体上，许多现有方法无法生成可用深度估计。
原文摘要 · Abstract (English)
Reliable depth perception is critical for robotic manipulation, especially for non-Lambertian objects such as transparent or highly specular surfaces, where raw depth measurements are often corrupted or missing. These failures frequently propagate to motion planning, resulting in invalid grasp poses and execution errors. We propose AISPO, a depth completion framework that improves depth reliability for manipulation in challenging sensing conditions. AISPO combines multi-scale RGB-D feature fusion with an affine-invariant shape prior to enforce geometric consistency and mitigate catastrophic depth failures. Unlike methods that focus primarily on average depth accuracy, our approach emphasizes physical plausibility and structural integrity of the predicted depth maps. Extensive benchmark evaluations demonstrate competitive performance and strong generalization to unseen objects and novel scenes. Real-world grasping experiments further show that enhanced depth reliability significantly improves manipulation success rates, particularly for transparent objects where many existing methods fail to produce physically usable depth estimates.
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