MaskNet: A Fully-Convolutional Network to Estimate Inlier Points

TL;DR

MaskNet is a fully-convolutional neural network designed to identify inlier points in point clouds, improving registration accuracy and demonstrating strong generalization on synthetic and real-world datasets.

Contribution

The paper introduces MaskNet, a novel neural network architecture that enhances point cloud registration by accurately identifying inlier points, applicable to various real-world scenarios.

Findings

Improves registration accuracy in point cloud matching

Demonstrates strong generalization to unseen datasets

Enhances both learning-based and classical registration methods

Abstract

Point clouds have grown in importance in the way computers perceive the world. From LIDAR sensors in autonomous cars and drones to the time of flight and stereo vision systems in our phones, point clouds are everywhere. Despite their ubiquity, point clouds in the real world are often missing points because of sensor limitations or occlusions, or contain extraneous points from sensor noise or artifacts. These problems challenge algorithms that require computing correspondences between a pair of point clouds. Therefore, this paper presents a fully-convolutional neural network that identifies which points in one point cloud are most similar (inliers) to the points in another. We show improvements in learning-based and classical point cloud registration approaches when retrofitted with our network. We demonstrate these improvements on synthetic and real-world datasets. Finally, our network produces impressive results on test datasets that were unseen during training, thus exhibiting generalizability. Code and videos are available at https://github.com/vinits5/masknet