Sparse Camera

Sparse camera systems aim to reconstruct 3D scenes and human motion from a limited number of camera views, overcoming the limitations and cost of dense camera setups. Current research focuses on developing robust algorithms, often incorporating diffusion models, neural implicit representations (like radiance fields and Gaussian splatting), and cascaded calibration techniques, to achieve accurate 3D reconstruction and novel view synthesis even with significant occlusion and depth ambiguity. These advancements are enabling high-quality real-time applications such as free-viewpoint rendering of humans for virtual and augmented reality, and improving efficiency in motion capture and robotic manipulation tasks. The resulting improvements in accuracy and efficiency have significant implications for various fields, including computer vision, graphics, and robotics.