HashPoint: Accelerated Point Searching and Sampling for Neural Rendering
This addresses the trade-off between speed and quality in neural rendering, offering a practical improvement for applications requiring real-time or high-fidelity rendering.
The paper tackles the problem of efficient point searching and sampling for volume neural rendering by combining rasterization and ray tracing, resulting in substantial speed-up for state-of-the-art ray-tracing-based methods while maintaining equivalent or superior accuracy.
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/.