Point cloud-based scene flow estimation on realistically deformable objects: A benchmark of deep learning-based methods.

Flow estimation on 3D point clouds is a challenging problem in the field of computer vision, which has great significance in many areas, such as autonomous driving and human interaction applications. Within the last years, the field of motion analysis has made great progress. The evaluation of the existing approaches mostly focuses on scenarios where objects are affected by rigid transformations. However, in many application areas such as gesture recognition or pose tracking, the detection of shape changes is essential and breaking them down to local rigid transformations is accompanied by loss of information. One component of our contributions is that we specifically prepared existing datasets for scene flow estimation on deformable objects. Additionally, we benchmark existing methods and analyze their behavior on various subtasks. The results show that already close to 80% of correct correspondences can be found on synthetic hand data, while only around 50% are found on real hand data. Our experimental validation and analysis help to build an understanding of new possibilities in broader areas. Furthermore, they should help to inspire possible further research directions. • Real human data challenges existing scene flow estimation methods. • Learning cost volumes reduces the runtime for calculating correspondences. • Occlusions of body parts pose different problems to scene flow estimation methods. • Generalization abilities of scene flow methods on human data are limited. [ABSTRACT FROM AUTHOR]