Point Clouds: Lidar versus 3D Vision

Abstract

Novel automated photogrammetry is based on four innovations. First is the cost-free increase of overlap between images when sensing digitally. Second is an improved radiometry. Third is multi-view matching. Fourth is the Graphics Processing Unit (GPU), making complex algorithms for image matching very practical. These innovations lead to improved automation of the photogrammetric workflow so that point clouds are created at sub-pixel accuracy, at very dense intervals, and in near real-time, thereby eroding the unique selling proposition of lidar scanners. Two test projects compare point clouds from aerial and street-side lidar systems with those created from images. We show that the photogrammetric accuracy compares well with the lidar-method, yet the density of surface points is much higher from images, and the throughput is commensurate with a fully automated all-digital approach. Beyond this density, we identify 15 additional advantages of the photogrammetric approach.