An immersive labeling method for large point clouds
Tianfang Lin, Zhongyuan Yu, Matthew McGinity, Stefan Gumhold
Abstract
3D point clouds, such as those produced by 3D scanners, often require labeling — the accurate classification of each point into structural or semantic categories – before they can be used in their intended application. However, in the absence of fully automated methods, such labeling must be performed manually, which can prove extremely time and labour intensive. To address this we present a virtual reality tool for accelerating and improving the manual labeling of very large 3D point clouds. The labeling tool provides a variety of 3D interactions for efficient viewing, selection and labeling of points using the controllers of consumer VR-kits. The main contribution of our work is a mixed CPU/GPU-based data structure that supports rendering, selection and labeling with immediate visual feedback at high frame rates necessary for a convenient VR experience. Our mixed CPU/GPU data structure supports fluid interaction with very large point clouds in VR, what is not possible with existing continuous level-of-detail rendering algorithms. We evaluate our method with 25 users on tasks involving point clouds of up to 50 million points and find convincing results that support the case for VR-based point cloud labeling. • A chunk-based compute shader selection algorithm for fast spatial queries and modification of subsets of large point clouds. • Acceleration of the continuous level-of-detail algorithm through the addition of CPU-based visibility culling. • Instantaneous visual feedback of user interactions, including undo and restore operations. • The efficiency of the labeling process is greatly improved in terms of positioning and detail observation.