Temporally Coherent General Dynamic Scene Reconstruction
This work addresses the challenge of general dynamic scene reconstruction for applications like free-viewpoint rendering and virtual reality, representing an incremental advance over existing controlled-environment techniques.
The paper tackles the problem of reconstructing complex dynamic scenes from multiple wide-baseline cameras without prior knowledge, achieving improved accuracy in multi-view segmentation and dense reconstruction compared to state-of-the-art methods.
Existing techniques for dynamic scene reconstruction from multiple wide-baseline cameras primarily focus on reconstruction in controlled environments, with fixed calibrated cameras and strong prior constraints. This paper introduces a general approach to obtain a 4D representation of complex dynamic scenes from multi-view wide-baseline static or moving cameras without prior knowledge of the scene structure, appearance, or illumination. Contributions of the work are: An automatic method for initial coarse reconstruction to initialize joint estimation; Sparse-to-dense temporal correspondence integrated with joint multi-view segmentation and reconstruction to introduce temporal coherence; and a general robust approach for joint segmentation refinement and dense reconstruction of dynamic scenes by introducing shape constraint. Comparison with state-of-the-art approaches on a variety of complex indoor and outdoor scenes, demonstrates improved accuracy in both multi-view segmentation and dense reconstruction. This paper demonstrates unsupervised reconstruction of complete temporally coherent 4D scene models with improved non-rigid object segmentation and shape reconstruction and its application to free-viewpoint rendering and virtual reality.