EGRA-NeRF: Edge-Guided Ray Allocation for Neural Radiance Fields

Recently, Neural Radiance Fields (NeRF) has demonstrated great potential in synthesizing novel views for realistic video generation. However, renderings from NeRF appear excessively blurred and contain aliasing artifacts in some textures or edges. To alleviate this problem, Edge-Guided Ray Allocation (EGRA-NeRF) module is proposed in this paper. Such method proposes a novel ray allocation strategy to focus more rays on the textures and edges of the scene during the training stage. Specially, the Canny edge detector is introduced to generate the ray guidance map. This guidance map can lead the model to find areas with more textures. To focus more rays on edges and textures, a method is explored to re-adjust the ray guidance map and re-allocate rays dynamically. Compared with the ray allocation in most existing NeRF-based methods, EGRA-NeRF focuses rays more on textures and edges, which can achieve the refinement of edges and texture details. Experiments show that NeRF-based algorithms (NeRF and Instant-NGP) with our EGRA module can achieve more realistic video generation, while maintaining almost the same computation time.