PointNeRF++: A multi-scale, point-based Neural Radiance Field (2312.02362v2)

Abstract: Point clouds offer an attractive source of information to complement images in neural scene representations, especially when few images are available. Neural rendering methods based on point clouds do exist, but they do not perform well when the point cloud quality is low -- e.g., sparse or incomplete, which is often the case with real-world data. We overcome these problems with a simple representation that aggregates point clouds at multiple scale levels with sparse voxel grids at different resolutions. To deal with point cloud sparsity, we average across multiple scale levels -- but only among those that are valid, i.e., that have enough neighboring points in proximity to the ray of a pixel. To help model areas without points, we add a global voxel at the coarsest scale, thus unifying ``classical'' and point-based NeRF formulations. We validate our method on the NeRF Synthetic, ScanNet, and KITTI-360 datasets, outperforming the state of the art, with a significant gap compared to other NeRF-based methods, especially on more challenging scenes.

• The paper presents a multi-scale framework that aggregates multi-resolution features to effectively fill gaps in sparse point clouds.
• It replaces coarse-scale MLPs with a tri-plane representation to efficiently cover larger regions without heavy computational costs.
• The approach unifies classical volumetric NeRF and point-based methods, demonstrating superior rendering quality on datasets like ScanNet and KITTI-360.

Neural Radiance Fields (NeRF) have been transformative in the field of computer vision by enabling high-quality novel-view synthesis from a set of images. However, situations with limited available views pose a considerable challenge, especially when using sparse or incomplete point clouds derived from real-world data. A recent development in neural rendering proposes a novel approach to address these challenges with a method called PointNeRF++.

The Challenge with Sparse Point Clouds

When dealing with real-world data, the point clouds obtained by methods such as LiDAR or photogrammetry often exhibit sparsity and incompleteness. Previous methods like PointNeRF demonstrated that point clouds could significantly enhance the rendering quality. Yet, they struggled when the density of the point clouds was uneven, or parts of the scene were unrepresented by any points.

Introducing PointNeRF++

PointNeRF++ tackles the difficulties of sparse point clouds with a multi-scale approach, creating a hierarchical representation of point data. The method takes inspiration from the multi-scale strategies often seen in point cloud processing, wherein large gaps in point distribution (referred to as 'holes') can be filled by aggregating information at multiple scales.

This technique leverages multiple voxel grids at varying resolutions to represent a scene, extending up to a global scale that encompasses the entire environment. By aggregating features across valid scales only, PointNeRF++ can naturally address areas where the point cloud is sparse or even missing.

Moreover, PointNeRF++ replaces the commonly used Multilayer Perceptron (MLP) at coarser scales with a tri-plane representation, which allows for more effective coverage of larger support regions without the significant computational overhead associated with large MLPs.

Unifying Classical and Point-Based NeRF Formulations

What makes PointNeRF++ particularly innovative is its incorporation of a global scale, akin to a traditional point-agnostic NeRF model. This effectively unites the classical volumetric and point-based NeRF approaches into a single, coherent framework. As a result, PointNeRF++ can effectively render regions with high point cloud density as well as those with none at all.

Superior Quality and Performance

The evaluation of PointNeRF++ across different datasets—including NeRF Synthetic, ScanNet, and KITTI-360—demonstrates its superior performance. In direct comparison to methods like PointNeRF and Gaussian Splatting, PointNeRF++ not only performs significantly better but also shows robustness to point cloud sparsity and incompleteness.

Conclusion

PointNeRF++ represents a significant advance in neural rendering, particularly for real-world scenarios where data could be sparse and incomplete. By integrating multi-scale modeling and a global scale into the architecture, this method opens the door for more efficient and practical use of NeRFs in various applications, from autonomous driving to virtual reality. The progress made by PointNeRF++ is a promising step toward more generalized and robust neural rendering solutions.