3D point cloud video segmentation oriented to the analysis of interactions

Given the widespread availability of point cloud data from consumer depth sensors, 3D point cloud segmentation becomes a promising building block for high level applications such as scene understanding and interaction analysis. It benefits from the richer information contained in real world 3D data compared to 2D images. This also implies that the classical color segmentation challenges have shifted to RGBD data, and new challenges have also emerged as the depth information is usually noisy, sparse and unorganized. Meanwhile, the lack of 3D point cloud ground truth labeling also limits the development and comparison among methods in 3D point cloud segmentation. In this paper, we present two contributions: a novel graph based point cloud segmentation method for RGBD stream data with interacting objects and a new ground truth labeling for a previously published data set. This data set focuses on interaction (merge and split between ’object’ point clouds), which differentiates itself from the few existing labeled RGBD data sets which are more oriented to Simultaneous Localization And Mapping (SLAM) tasks. The proposed point cloud segmentation method is evaluated with the 3D point cloud ground truth labeling. Experiments show the promising result of our approach.Postprint (published version

A LiDAR Point Cloud Generator: from a Virtual World to Autonomous Driving

3D LiDAR scanners are playing an increasingly important role in autonomous driving as they can generate depth information of the environment. However, creating large 3D LiDAR point cloud datasets with point-level labels requires a significant amount of manual annotation. This jeopardizes the efficient development of supervised deep learning algorithms which are often data-hungry. We present a framework to rapidly create point clouds with accurate point-level labels from a computer game. The framework supports data collection from both auto-driving scenes and user-configured scenes. Point clouds from auto-driving scenes can be used as training data for deep learning algorithms, while point clouds from user-configured scenes can be used to systematically test the vulnerability of a neural network, and use the falsifying examples to make the neural network more robust through retraining. In addition, the scene images can be captured simultaneously in order for sensor fusion tasks, with a method proposed to do automatic calibration between the point clouds and captured scene images. We show a significant improvement in accuracy (+9%) in point cloud segmentation by augmenting the training dataset with the generated synthesized data. Our experiments also show by testing and retraining the network using point clouds from user-configured scenes, the weakness/blind spots of the neural network can be fixed