In this paper, we address the problem of efficient point searching and sampling for volume neural rendering. Within this realm, two typical approaches are employed: rasterization and ray tracing. The rasterization-based methods enable real-time rendering at the cost of increased memory and lower fidelity. In contrast, the ray-tracing-based methods yield superior quality but demand longer rendering time. We solve this problem by our HashPoint method combining these two strategies, leveraging rasterization for efficient point searching and sampling, and ray marching for rendering. Our method optimizes point searching by rasterizing points within the camera's view, organizing them in a hash table, and facilitating rapid searches. Notably, we accelerate the rendering process by adaptive sampling on the primary surface encountered by the ray. Our approach yields substantial speed-up for a range of state-of-the-art ray-tracing-based methods, maintaining equivalent or superior accuracy across synthetic and real test datasets. The code will be available at https://jiahao-ma.github.io/hashpoint/.

翻译：本文针对体神经渲染中的高效点搜索与采样问题展开研究。在该领域中，存在两种典型方法：光栅化与光线追踪。基于光栅化的方法可实现实时渲染，但以增加内存消耗和降低保真度为代价；而基于光线追踪的方法虽能获得更优质量，却需要更长的渲染时间。我们提出HashPoint方法融合两种策略：利用光栅化实现高效点搜索与采样，同时采用光线步进完成渲染。该方法通过光栅化相机视野内的点、将其组织成哈希表来实现快速搜索，从而优化了点搜索过程。值得注意的是，我们通过对光线首次触及的主表面进行自适应采样来加速渲染流程。该方法能够显著提升一系列先进光线追踪方法的渲染速度，在合成数据集与真实测试集上均保持同等或更优的精度。代码将发布于https://jiahao-ma.github.io/hashpoint/。