Shape-Biased Texture Agnostic Representations for Improved Textureless and Metallic Object Detection and 6D Pose Estimation

Recent advances in machine learning have greatly benefited object detection and 6D pose estimation. However textureless and metallic objects still pose a significant challenge due to few visual cues and the texture bias of CNNs. To address this issue we propose a strategy for inducing a shape bias to CNN training. In particular by randomizing textures applied to object surfaces during data rendering we create training data without consistent textural cues. This methodology allows for seamless integration into existing data rendering engines and results in negligible computational overhead for data rendering and network training. Our findings demonstrate that the shape bias we induce via randomized texturing improves over existing approaches using style transfer. We evaluate with five detectors and two pose estimators. For three object detectors and for pose estimation in general estimation accuracy improves for textureless and metallic objects. Additionally we show that our approach increases the pose estimation accuracy in the presence of image noise and strong illumination changes. Code available at https://github.com/hoenigpeter/randomized_texturing.