Photorealistic reconstruction relying on 3D Gaussian Splatting has shown
promising potential in robotics. However, the current 3D Gaussian Splatting
system only supports radiance field reconstruction using undistorted
perspective images. In this paper, we present OmniGS, a novel omnidirectional
Gaussian splatting system, to take advantage of omnidirectional images for fast
radiance field reconstruction. Specifically, we conduct a theoretical analysis
of spherical camera model derivatives in 3D Gaussian Splatting. According to
the derivatives, we then implement a new GPU-accelerated omnidirectional
rasterizer that directly splats 3D Gaussians onto the equirectangular screen
space for omnidirectional image rendering. As a result, we realize
differentiable optimization of the radiance field without the requirement of
cube-map rectification or tangent-plane approximation. Extensive experiments
conducted in egocentric and roaming scenarios demonstrate that our method
achieves state-of-the-art reconstruction quality and high rendering speed using
omnidirectional images. To benefit the research community, the code will be
made publicly available once the paper is published.

本文介绍了一种新的全向高斯散乱系统 ——OmniGS，它利用全向图像进行快速辐射场重建，通过对全向相机模型导数的理论分析，在 GPU 加速的全向光栅化器中直接对高斯函数进行散乱，实现了辐射场的可微优化，无需立方体贴图矫正或切平面近似。实验结果表明，该方法在自我中心和移动场景中实现了最先进的重建质量和高渲染速度，并在论文发表后将公开提供代码。

OmniGS：使用全向图像进行快速辐射场重建的全向高斯喷洒

OmniGS: Omnidirectional Gaussian Splatting for Fast Radiance Field  Reconstruction using Omnidirectional Images

3D Gaussian Splatting (3DGS) has recently revolutionized radiance field
reconstruction, achieving high quality novel view synthesis and fast rendering
speed without baking. However, 3DGS fails to accurately represent surfaces due
to the multi-view inconsistent nature of 3D Gaussians. We present 2D Gaussian
Splatting (2DGS), a novel approach to model and reconstruct geometrically
accurate radiance fields from multi-view images. Our key idea is to collapse
the 3D volume into a set of 2D oriented planar Gaussian disks. Unlike 3D
Gaussians, 2D Gaussians provide view-consistent geometry while modeling
surfaces intrinsically. To accurately recover thin surfaces and achieve stable
optimization, we introduce a perspective-accurate 2D splatting process
utilizing ray-splat intersection and rasterization. Additionally, we
incorporate depth distortion and normal consistency terms to further enhance
the quality of the reconstructions. We demonstrate that our differentiable
renderer allows for noise-free and detailed geometry reconstruction while
maintaining competitive appearance quality, fast training speed, and real-time
rendering. Our code will be made publicly available.

通过将三维体积转化为一组二维定向平面高斯盘，使用透视准确的二维喷洒过程，结合深度扭曲和法线一致性项，实现从多视角图像中准确重建细表面并提高重建质量的一种方法。

二维高斯粒子扩散用于几何精确辐射场

2D Gaussian Splatting for Geometrically Accurate Radiance Fields

NeRFs have revolutionized the world of per-scene radiance field
reconstruction because of their intrinsic compactness. One of the main
limitations of NeRFs is their slow rendering speed, both at training and
inference time. Recent research focuses on the optimization of an explicit
voxel grid (EVG) that represents the scene, which can be paired with neural
networks to learn radiance fields. This approach significantly enhances the
speed both at train and inference time, but at the cost of large memory
occupation. In this work we propose Re:NeRF, an approach that specifically
targets EVG-NeRFs compressibility, aiming to reduce memory storage of NeRF
models while maintaining comparable performance. We benchmark our approach with
three different EVG-NeRF architectures on four popular benchmarks, showing
Re:NeRF's broad usability and effectiveness.

本文提出了 Re:NeRF 方法，用于在维持性能的同时降低 NeRF 模型的内存存储。与三种不同的 EXG-NeRF 体系结构在四个流行的基准测试中进行基准测试，显示了 Re:NeRF 的广泛适用性和有效性。