Tag Archives: 3d Point Clouds

Resampling point clouds to reduce their size without compromising their utility for the tasks that use them

S. Chen, D. Tian, C. Feng, A. Vetro and J. Kovačević, Fast Resampling of Three-Dimensional Point Clouds via Graphs, IEEE Transactions on Signal Processing, vol. 66, no. 3, pp. 666-681, DOI: 10.1109/TSP.2017.2771730.

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 graphs, which can represent underlying surfaces and lend themselves well to efficient computation. We use a general feature-extraction operator to represent application-dependent features and propose a general reconstruction error to evaluate the quality of resampling; by minimizing the error, we obtain a general form of optimal resampling distribution. The proposed resampling distribution is guaranteed to be shift-, rotation- and scale-invariant in the three-dimensional space. We then specify the feature-extraction operator to be a graph filter and study specific resampling strategies based on all-pass, low-pass, high-pass graph filtering and graph filter banks. We validate the proposed methods on three applications: Large-scale visualization, accurate registration, and robust shape modeling demonstrating the effectiveness and efficiency of the proposed resampling methods.

A new feature for 3D point clouds that is more efficient than the state-of-the-art SHOT

Sai Manoj Prakhya, Bingbing Liu, Weisi Lin, Vinit Jakhetiya & Sharath Chandra Guntuku, B-SHOT: a binary 3D feature descriptor for fast Keypoint matching on 3D point clouds, Auton Robot (2017) 41:1501–1520, DOI: 10.1007/s10514-016-9612-y.

We present the first attempt in creating a binary 3D feature descriptor for fast and efficient keypoint matching on 3D point clouds. Specifically, we propose a linarization technique and apply it on the state-of-the-art 3D feature descriptor, SHOT to create the first binary 3D feature descriptor, which we call B-SHOT. B-SHOT requires 32 times lesser memory for its representation while being six times faster in feature descriptor matching, when compared to the SHOT feature descriptor. Next, we propose a robust evaluation metric, specifically for 3D feature descriptors. A comprehensive evaluation on standard benchmarks reveals that B-SHOT offers comparable keypoint matching performance to that of the state-of-the-art real valued 3D feature descriptors, albeit at dramatically lower computational and memory costs.