STAR: Shape-focused Texture Agnostic Representations for Improved Object Detection and 6D Pose Estimation

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 …

advances bias challenge cnns cs.cv detection issue machine machine learning objects robotic star texture visual visual cues