In this follow-up on our CVPR’18 work, we extend our weakly-supervised 3D shape completion approach to obtain high-quality shape predictions, and also present updated, synthetic benchmarks on ShapeNet and ModelNet. The paper is now available as pre-print on ArXiv. Abstract, some experimental results and a comparison to our CVPR’18 work can be found in this article.
In this CVPR’18 paper, based on my master thesis, we propose a weakly-supervised and learning-based approach to 3D shape completion of sparse and noisy point clouds. We show that, using a learned shape prior, shape completion can be learned without access to ground truth shapes — only by knowing the object category at hand. This article provides the paper and its supplementary material.
My master thesis, written at the Autonomous Vision Group of Max Planck Institute for Intelligent Systems under the supervision of Prof. Andreas Geiger, addresses the problem of 3D shape completion of sparse point clouds under weak supervision. Specifically, based on a learned shape prior it is possible to learn 3D shape completion without access to ground truth shapes, as shown on KITTI. This article briefly introduces the problem and the main contributions and offers the thesis as download.
This article discusses how to visualize triangular meshes available in Object File Format (
.off) in Python using occmodel. Installation instructions for installing occmodel on Ubuntu are included.