上海科技大学知识管理系统

Expanding upon the foundational Neural Radiance Fields (NeRF) framework, neural rendering techniques have seen wide-ranging applications in 3D reconstruction and rendering. Despite the numerous attempts to accelerate the original NeRF, the ray marching process in many neural rendering algorithms remains a performance bottleneck, constraining their rendering speed. In this work, we introduce an innovative method for effective sampling based on density estimation to alleviate this bottleneck. The proposed method can effectively reduce the number of required occupancy grid access without compromising rendering quality. Evaluation and analysis results validate the effectiveness of the proposed approach, delivering notable improvements in rendering efficiency.