  This paper presents NGP-RT, a novel approach for enhancing the rendering
speed of Instant-NGP to achieve real-time novel view synthesis. As a classic
NeRF-based method, Instant-NGP stores implicit features in multi-level grids or
hash tables and applies a shallow MLP to convert the implicit features into
explicit colors and densities. Although it achieves fast training speed, there
is still a lot of room for improvement in its rendering speed due to the
per-point MLP executions for implicit multi-level feature aggregation,
especially for real-time applications. To address this challenge, our proposed
NGP-RT explicitly stores colors and densities as hash features, and leverages a
lightweight attention mechanism to disambiguate the hash collisions instead of
using computationally intensive MLP. At the rendering stage, NGP-RT
incorporates a pre-computed occupancy distance grid into the ray marching
strategy to inform the distance to the nearest occupied voxel, thereby reducing
the number of marching points and global memory access. Experimental results
show that on the challenging Mip-NeRF360 dataset, NGP-RT achieves better
rendering quality than previous NeRF-based methods, achieving 108 fps at 1080p
resolution on a single Nvidia RTX 3090 GPU. Our approach is promising for
NeRF-based real-time applications that require efficient and high-quality
rendering.
