Constructing point clouds from underwater stereo movies

Abstract

Processing images of underwater environments of Antarctic lakes is challenging due to poor lighting conditions, low saturation and noise. This paper presents a novel pipeline for dense point cloud scene reconstruction from underwater stereo images and video obtained with low-cost consumer recording hardware. Features in stereo frames are selected and matched at high density. Putative matches are triangulated to produce point clouds in 3D space. Temporal feature tracking is performed to produce and merge point clouds. We demonstrate that this framework produces dense and accurate reconstructions for several tests.