Sparse RGB

Sparse RGB research focuses on reconstructing complete and realistic 3D scenes or human models from limited, sparsely sampled RGB (and sometimes depth) camera views. Current efforts leverage neural networks, including diffusion models and neural rendering techniques, often incorporating techniques like view synthesis, feature distillation, and iterative refinement to overcome the inherent ambiguities of sparse data. This work is significant for advancing applications in telepresence, virtual and augmented reality, and robotic manipulation by enabling high-fidelity 3D scene generation and human performance capture with reduced hardware requirements.