Part of my master thesis at the Max Planck Institute for Intelligent Systems was an initial proposal — outlining the general idea and the current state-of-the-art. Specifically, I worked on learning 3D shape completion on KITTI using 3D bounding boxes only. In this article, I want to present this proposal.
As part of my stay in the Autonomous Vision Group in Tübingen, I also worked on the KITTI dataset. With the help of Bo Li, we were able to add a novel benchmark: the 3D object detection benchmark. In this article, I briefly want to introduce the benchmark and give some useful hints regarding submission.
Deep learning on 3D data is still challenging — partly due to the computational complexity of training on 3D grids. Thus, alternative data structures become more and more interesting. PointNets, for example, operate directly on unordered set of points, i.e. point clouds. Similarly, Point Set Generation Networks are able to directly predict point clouds from images. In this article, I briefly summarize both ideas.