Learning 3D-3D Correspondences for One-shot Partial-to-partial Registration
arXiv:2006.04523
Abstract
While 3D-3D registration is traditionally tacked by optimization-based methods, recent work has shown that learning-based techniques could achieve faster and more robust results. In this context, however, only PRNet can handle the partial-to-partial registration scenario. Unfortunately, this is achieved at the cost of relying on an iterative procedure, with a complex network architecture. Here, we show that learning-based partial-to-partial registration can be achieved in a one-shot manner, jointly reducing network complexity and increasing registration accuracy. To this end, we propose an Optimal Transport layer able to account for occluded points thanks to the use of outlier bins. The resulting OPRNet framework outperforms the state of the art on standard benchmarks, demonstrating better robustness and generalization ability than existing techniques.
11 pages
References in corpus (6)
- PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
- Open3D: A Modern Library for 3D Data Processing
- Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration
- PRNet: Self-Supervised Learning for Partial-to-Partial Registration
- 3D ShapeNets: A Deep Representation for Volumetric Shapes
- Learning 2D-3D Correspondences To Solve The Blind Perspective-n-Point Problem