Temporally coherent 4D reconstruction of complex dynamic scenes
This addresses the challenge of creating complete 4D representations for unstructured indoor and outdoor scenes with multiple people, which is incremental as it builds on existing reconstruction methods.
The paper tackles the problem of reconstructing 4D temporally coherent models of complex dynamic scenes from multiple moving cameras without prior knowledge, achieving improved nonrigid object segmentation and shape reconstruction.
This paper presents an approach for reconstruction of 4D temporally coherent models of complex dynamic scenes. No prior knowledge is required of scene structure or camera calibration allowing reconstruction from multiple moving cameras. Sparse-to-dense temporal correspondence is integrated with joint multi-view segmentation and reconstruction to obtain a complete 4D representation of static and dynamic objects. Temporal coherence is exploited to overcome visual ambiguities resulting in improved reconstruction of complex scenes. Robust joint segmentation and reconstruction of dynamic objects is achieved by introducing a geodesic star convexity constraint. Comparative evaluation is performed on a variety of unstructured indoor and outdoor dynamic scenes with hand-held cameras and multiple people. This demonstrates reconstruction of complete temporally coherent 4D scene models with improved nonrigid object segmentation and shape reconstruction.