Neural Radiance Fields with Torch Units (2404.02617v1)

Abstract: 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.

• The paper introduces Torch-NeRF, a novel approach that enriches camera ray context by rendering patches instead of single pixels for improved scene reconstruction.
• It employs distance-aware convolutions to replace traditional MLPs, enabling dynamic interactions among sample points along each ray.
• Experimental results on KITTI-360 and LLFF datasets demonstrate significant improvements in structure preservation and noise reduction in complex scenes.

Exploring Neural Radiance Fields with Torch Units for Large-Scale Scenes

Introduction to Neural Radiance Fields with Torch Units (Torch-NeRF)

Neural Radiance Fields (NeRF) have become a pivotal technology in synthesizing novel photo-realistic views from sparse samples, with applications stretching from virtual reality to autonomous driving simulations. However, the task of reconstructing complex, large-scale scenes remains daunting. Traditional methods, leveraging implicit volume rendering without considering the interaction among sample points, suffer when faced with the intricate variability commonly found in large scenes, particularly those in autonomous driving contexts.

Addressing these limitations, our focus shifts towards a novel inference pattern designed to enrich a single camera ray with an extensive context, theoretically and practically enhancing the capability of a ray to render a patch rather than a single pixel. This approach, dubbed the Torch-NeRF, ventures into enlarging the ray perception field and facilitating interactions among sample points via distance-aware convolutions. This innovative method bears witness to the elevation of both theoretical and practical facades of neural radiance field technology, paving a path towards adept reconstruction of voluminous and complex scenes.

Enlarging the Ray Perception Field

The paradigm shift introduces a method where each camera ray renders a patch of pixels, a notable departure from the traditional approach where a ray is aligned with a singular pixel. This significantly enhances the contextual information available per ray, providing a more nuanced understanding of the scene's geometry and appearance. The process begins with transforming input coordinates into a neural network, producing a patch of colors and densities, which are then composited into pixel patches through volume rendering techniques.

Distance-Aware Convolutions Along Rays

Another cornerstone of our method is the replacement of the conventional multi-layer perceptron (MLP) models with distance-aware convolutions. This adjustment allows for dynamic feature interaction among sample points on the same camera ray, factoring in the distances between points to smooth out the distribution of volumes and suppress noise space occupancies. Such convolutional operations weave a complex relationship among sample points, enhancing the quality and accuracy of the rendered images.

Extensive Experimental Justification

The Torch-NeRF exhibits exemplary performance across various benchmarks, notably on KITTI-360 and LLFF datasets, which are notorious for their complex backgrounds. The model significantly improves upon its predecessors not only in generating high-fidelity reconstructions but also in efficiently handling large-scale scenes. It achieves this through qualitative improvements in structure preservation, noise reduction under challenging lighting conditions, and better handling of scene edges.

Implications and Future Directions

The conception of Torch-NeRF marks a significant stride in our ongoing journey to master large-scale 3D reconstructions. Its ability to imbibe a broader context and foster intricate interactions among sample points illuminates the path for developing more efficient and robust neural radiance fields. As we peer into the horizon, the exploration of strategies for improving the rendering quality of all pixels in the patch and boosting model efficiency presents a promising avenue for future research.

In conclusion, the Torch-NeRF stands as a beacon of innovation in the domain of neural radiance fields, offering a scalable and efficient solution to the challenging task of reconstructing complex, large-scale scenes. Its design philosophy and achieved benchmarks pave the way for future advancements, setting a foundational stone for the development of more sophisticated and capable models in the generative AI space.