Neural Mesh-Based Graphics
This work addresses the problem of fast and data-efficient view synthesis for computer graphics applications, representing an incremental improvement over existing neural rendering methods.
The paper tackles data-efficient novel view synthesis by introducing a mesh-based denser point descriptor rasterization with a foreground/background split and improved loss, achieving competitive results with state-of-the-art methods while training on only a single scene.
We revisit NPBG, the popular approach to novel view synthesis that introduced the ubiquitous point feature neural rendering paradigm. We are interested in particular in data-efficient learning with fast view synthesis. We achieve this through a view-dependent mesh-based denser point descriptor rasterization, in addition to a foreground/background scene rendering split, and an improved loss. By training solely on a single scene, we outperform NPBG, which has been trained on ScanNet and then scene finetuned. We also perform competitively with respect to the state-of-the-art method SVS, which has been trained on the full dataset (DTU and Tanks and Temples) and then scene finetuned, in spite of their deeper neural renderer.