Neural Radiance Fields with Torch Units
Bingnan Ni Huanyu Wang Dongfeng Bai Minghe Weng Dexin Qi Weichao Qiu Bingbing Liu
Neural Radiance Fields (NeRF) give rise to learning-based 3D reconstruction
methods widely used in industrial applications. Although prevalent methods
achieve considerable improvements in small-scale scenes, accomplishing
reconstruction in complex and large-scale scenes is still challenging. First,
the background in complex scenes shows a large variance among different views.
Second, the current inference pattern, $i.e.$, a pixel only relies on an
individual camera ray, fails to capture contextual information. To solve these
problems, we propose to enlarge the ray perception field and build up the
sample points interactions. In this paper, we design a novel inference pattern
that encourages a single camera ray possessing more contextual information, and
models the relationship among sample points on each camera ray. To hold
contextual information,a camera ray in our proposed method can render a patch
of pixels simultaneously. Moreover, we replace the MLP in neural radiance field
models with distance-aware convolutions to enhance the feature propagation
among sample points from the same camera ray. To summarize, as a torchlight, a
ray in our proposed method achieves rendering a patch of image. Thus, we call
the proposed method, Torch-NeRF. Extensive experiments on KITTI-360 and LLFF
show that the Torch-NeRF exhibits excellent performance.