基于几何先验优化视图合成的场景配置，提升室内环境渲染质量。
Geometry-Aware Scene Configurations for Novel View Synthesis
- 利用几何骨架引导基础点自适应分布，替代均匀布局
- 在多个大型室内场景中实现更优渲染质量与内存效率
- 适合需要高效生成沉浸式室内视图的场景建模应用
我们提出一种场景自适应策略，以高效分配表示容量，从不完整观测中生成沉浸式室内环境。室内场景常具有不规则布局、复杂结构、杂物遮挡和平坦墙面。通过利用预处理阶段获取的几何先验，最大化有限资源的利用率。我们在估计的几何骨架上记录观测统计信息，并指导基点的最优部署，显著优于以往可扩展神经辐射场（NeRF）采用的均匀基点排列。同时，我们提出场景自适应虚拟视角，以弥补输入轨迹中固有的几何缺陷并施加必要正则化。我们在多个大规模室内场景中进行了全面分析，验证了该方法在渲染质量和内存需求方面相比基线方法有显著提升。
原文摘要 · Abstract (English)
We propose scene-adaptive strategies to efficiently allocate representation capacity for generating immersive experiences of indoor environments from incomplete observations. Indoor scenes with multiple rooms often exhibit irregular layouts with varying complexity, containing clutter, occlusion, and flat walls. We maximize the utilization of limited resources with guidance from geometric priors, which are often readily available after pre-processing stages. We record observation statistics on the estimated geometric scaffold and guide the optimal placement of bases, which greatly improves upon the uniform basis arrangements adopted by previous scalable Neural Radiance Field (NeRF) representations. We also suggest scene-adaptive virtual viewpoints to compensate for geometric deficiencies inherent in view configurations in the input trajectory and impose the necessary regularization. We present a comprehensive analysis and discussion regarding rendering quality and memory requirements in several large-scale indoor scenes, demonstrating significant enhancements compared to baselines that employ regular placements. Project page is available at: https://mkjjang3598.github.io/Geo-Scene-Config.
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