VR 2017

2017 IEEE Virtual Reality (VR), March 18-22, 2017, Los Angeles, CA, USA

Desktop Layout

Motion Tracking and Capturing
Conference Papers
Ballroom A/B, Chair: Regis Kopper
Optimizing Placement of Commodity Depth Cameras for Known 3D Dynamic Scene Capture
Rohan Chabra, Adrian Ilie, Nicholas Rewkowski, Young-Woon Cha, and Henry Fuchs
(University of North Carolina at Chapel Hill, USA)
Preprint
Video
Supplementary Material
Abstract: Commodity depth sensors, such as the Microsoft Kinect®, have been widely used for the capture and reconstruction of the 3D structure of a room-sized dynamic scene. Camera placement and coverage during capture significantly impact the quality of the resulting reconstruction. In particular, dynamic occlusions and sensor interference have been shown to result in poor resolution and holes in the reconstruction results. This paper presents a novel algorithmic framework and an off-line optimization of depth sensor placements for a given 3D dynamic scene, simulated using virtual 3D models. We derive a fitness metric for a particular configuration of sensors by combining factors such as visibility and resolution of the entire dynamic scene along with probabilities of interference between sensors. We employ this fitness metric both in a greedy algorithm that determines the number of depth sensors needed to cover the scene, and in a simulated annealing algorithm that optimizes the placements of those sensors. We compare our algorithm’s optimized placements with manual sensor placements for a real dynamic scene. We present quantitative assessments using our fitness metric, as well as qualitative assessments to demonstrate that our algorithm not only enhances the resolution and total coverage of the reconstruction but also fills in voids by avoiding occlusions and sensor interference when compared with the reconstruction of the same scene using a manual sensor placement.

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Time stamp: 2019-07-19T00:06:34+02:00