论文标题
基于神经网格的图形
Neural Mesh-Based Graphics
论文作者
论文摘要
我们重新审视NPBG，这是一种流行的新型视图合成方法，引入了无处不在的点神经渲染范式。我们对具有快速视图合成的数据效率学习特别感兴趣。除了前景/背景场景渲染分裂以及改善的损失外，我们还通过基于视图的网格点描述符栅格化来实现这一目标。通过仅在单个场景上训练，我们的表现就超过了在扫描仪上接受过培训的NPBG，然后进行了固定场景。我们还针对最先进的方法SVS进行了竞争性，该方法已在完整的数据集（DTU，坦克和寺庙）上进行了培训，然后将场景进行了填补，尽管它们具有更深的神经渲染器。
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.
