Learning 3D shape completion under weak supervision; on ShapeNet, ModelNet, KITTI and Kinect data; published at CVPR and on ArXiv.
Finally, we are able to release the code and the data corresponding to our CVPR’18 paper on “Learning 3D Shape Completion from Laser Scan Data with Weak Supervision”. In this article, I want to briefly outline the released code and data.
Weakly-supervised shape completion of cars on KITTI using variational auto-encoders; including two synthetic ShapeNet-based benchmark datasets.
This article is a collection of Torch examples meant as introduction to get started with Lua and Torch for deep learning research. The examples can also be considered individually and cover common use cases such as training on CPU and GPU, weight initialization and visualization, custom modules and criteria as well as saving and fine-tuning models.