/PCN: Point Completion Network

PCN: Point Completion Network

Wentao Yuan, Tejas Khot, David Held, Christoph Mertz and Martial Hebert
Conference Paper, Proceedings of 2018 International Conference on 3D Vision (3DV), September, 2018

Download Publication (PDF)

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author’s copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract

Shape completion, the problem of estimating the complete geometry of objects from partial observations, lies at the core of many vision and robotics applications. In this work, we propose Point Completion Network (PCN), a novel learning-based approach for shape completion. Unlike existing shape completion methods, PCN directly operates on raw point clouds without any structural assumption (e.g. symmetry) or annotation (e.g. semantic class) about the underlying shape. It features a decoder design that enables the generation of fine-grained completions while maintaining a small number of parameters. Our experiments show that PCN produces dense, complete point clouds with realistic structures in the missing regions on inputs with various levels of incompleteness and noise, including cars from LiDAR scans in the KITTI dataset.

BibTeX Reference
@conference{Yuan-2018-110219,
author = {Wentao Yuan, Tejas Khot, David Held, Christoph Mertz, Martial Hebert},
title = {PCN: Point Completion Network},
booktitle = {Proceedings of 2018 International Conference on 3D Vision (3DV)},
year = {2018},
month = {September},
keywords = {Shape Completion, 3D Deep Learning},
}
2018-11-29T12:41:29+00:00