STAR: Shape-focused Texture Agnostic Representations for Improved Object
  Detection and 6D Pose Estimation
Peter Hönig Stefan Thalhammer Jean-Baptiste Weibel Matthias Hirschmanner Markus Vincze
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.