DeepMapping: Unsupervised Map Estimation From Multiple Point Clouds

We propose DeepMapping, a novel registration framework using deep neural networks (DNNs) as auxiliary functions to align multiple point clouds from scratch to a globally consistent frame. We use DNNs to model the highly non-convex mapping process …

FoldingNet: Point Cloud Auto-encoder via Deep Grid Deformation

The state-of-the-art unsupervised deep auto-encoder of point clouds which reconstruct order point clouds from unordered input, useful for autonomous driving, robotic scene understanding, etc.

Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling

We propose two new operations, Kernel Correlation and Graph Pooling, to efficiently and robustly improve PointNet, useful for autonomous driving, robotic scene understanding, etc.

FasTFit: A fast T-spline fitting algorithm

T-spline has been recently developed to represent objects of arbitrary shapes using a smaller number of control points than the conventional NURBS or B-spline representations in computer aided design, computer graphics, and reverse engineering. …

Fast Resampling of Three-Dimensional Point Clouds via Graphs

To reduce the cost of storing, processing and visualizing a large-scale point cloud, we propose a randomized resampling strategy that selects a representative subset of points while preserving application-dependent features. The strategy is based on …

Fast plane extraction in organized point clouds using agglomerative hierarchical clustering

The fastest plane detection algorithm on single-core CPU (35Hz for VGA size) for organized point clouds.

Point-plane SLAM for hand-held 3D sensors