Probabilistic Multi-View Fusion of Active Stereo Depth Maps
Jun Yang, Dong Li and Steven L. Waslander.
IEEE Robotics and Automation Letters (RA-L), 2021
The reliable fusion of depth maps from multiple viewpoints has become an important problem in many 3D reconstruction pipelines. In this work, we investigate its impact on robotic bin-picking tasks such as 6D object pose estimation. In this work, we propose a novel probabilistic framework for scene reconstruction in robotic bin-picking. Based on active stereo camera data, we first explicitly estimate the uncertainty of depth measurements, and incorporated into a probabilistic model for incrementally updating the scene. To extensively evaluate different fusion methods, we constructed ROBI (Reflective Objects in BIns), a multi-view dataset consisting of reflective objects in bin-picking scenes. The ROBI dataset will be released.