Indoor and outdoor depth imaging of leaves with time-of-flight and stereo vision sensors: analysis and comparison

In this article we analyze the response of Time-of-Flight (ToF) cameras (active sensors) for close range imaging under three different illumination conditions and compare the results with stereo vision (passive) sensors. ToF cameras are sensitive to ambient light and have low resolution but deliver high frame rate accurate depth data under suitable conditions. We introduce metrics for performance evaluation over a small region of interest. Based on these metrics, we analyze and compare depth imaging of leaf under indoor (room) and outdoor (shadow and sunlight) conditions by varying exposure times of the sensors. Performance of three different ToF cameras (PMD CamBoard, PMD CamCube and SwissRanger SR4000) is compared against selected stereo-correspondence algorithms (local correlation and graph cuts). PMD CamCube has better cancelation of sunlight, followed by CamBoard, while SwissRanger SR4000 performs poorly under sunlight. Stereo vision is comparatively more robust to ambient illumination and provides high resolution depth data but is constrained by texture of the object along with computational efficiency. Graph cut based stereo correspondence algorithm can better retrieve the shape of the leaves but is computationally much more expensive as compared to local correlation. Finally, we propose a method to increase the dynamic range of ToF cameras for a scene involving both shadow and sunlight exposures at the same time by taking advantage of camera flags (PMD) or confidence matrix (SwissRanger). (C) 2013 International Society for Photogrammetly and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved.Peer ReviewedPostprint (author’s final draft

Fast Realization of Digital Elevation Model

International audienceWe propose an optimization approach to speed up the point matching process underlying the 3D reconstruction of complex urban scenes. We consider the Optical Flow technique for point matching and propose to introduce MMX and SSE2 instructions to accelerate significantly the matching process. Fast point matching allows using sub-pixel image resolution, which provides a more accurate estimation of the Optical Flow by exploiting wider correlation windows, and therefore improves the final quality of urban scenes 3D reconstructions