Neural Pixel Composition: 3D-4D View Synthesis from Multi-Views
This addresses the need for efficient and robust view synthesis in computer vision, with incremental improvements in speed and sparsity handling.
The paper tackles the problem of 3D-4D view synthesis from sparse multi-view observations, achieving results comparable to or better than existing methods while training 200-400 times faster for high-resolution content.
We present Neural Pixel Composition (NPC), a novel approach for continuous 3D-4D view synthesis given only a discrete set of multi-view observations as input. Existing state-of-the-art approaches require dense multi-view supervision and an extensive computational budget. The proposed formulation reliably operates on sparse and wide-baseline multi-view imagery and can be trained efficiently within a few seconds to 10 minutes for hi-res (12MP) content, i.e., 200-400X faster convergence than existing methods. Crucial to our approach are two core novelties: 1) a representation of a pixel that contains color and depth information accumulated from multi-views for a particular location and time along a line of sight, and 2) a multi-layer perceptron (MLP) that enables the composition of this rich information provided for a pixel location to obtain the final color output. We experiment with a large variety of multi-view sequences, compare to existing approaches, and achieve better results in diverse and challenging settings. Finally, our approach enables dense 3D reconstruction from sparse multi-views, where COLMAP, a state-of-the-art 3D reconstruction approach, struggles.