Recent advances in machine learning have greatly benefited object detection and 6D pose estimation for robotic grasping. However, textureless and metallic objects still pose a significant challenge due to fewer visual cues and the texture bias of CNNs. To address this issue, we propose a texture-agnostic approach that focuses on learning from CAD models and emphasizes object shape features. To achieve a focus on learning shape features, the textures are randomized during the rendering of the training data. By treating the texture as noise, the need for real-world object instances or their final appearance during training data generation is eliminated. The TLESS and ITODD datasets, specifically created for industrial settings in robotics and featuring textureless and metallic objects, were used for evaluation. Texture agnosticity also increases the robustness against image perturbations such as imaging noise, motion blur, and brightness changes, which are common in robotics applications. Code and datasets are publicly available at github.com/hoenigpeter/randomized_texturing.

翻译：近年来机器学习的进展极大地推动了机器人抓取中的目标检测与6D姿态估计。然而，无纹理和金属物体仍然构成重大挑战，原因在于视觉线索较少以及CNN的纹理偏差。为解决这一问题，我们提出了一种纹理无关的方法，该方法侧重于从CAD模型学习并强调物体形状特征。为实现对形状特征学习的聚焦，在训练数据渲染过程中对纹理进行随机化处理。通过将纹理视为噪声，消除了对真实物体实例或其最终外观在训练数据生成阶段的需求。本研究采用专为工业机器人场景创建、包含无纹理和金属物体的TLESS与ITODD数据集进行评估。纹理无关性还增强了对成像噪声、运动模糊和亮度变化等机器人应用中常见图像扰动的鲁棒性。代码与数据集已公开在github.com/hoenigpeter/randomized_texturing。