Abstract
In this paper, we present a generalizable method for 3D surface reconstruction from raw point clouds or pre-estimated 3D Gaussians by 3DGS from RGB images. Unlike existing coordinate-based methods which are often computationally intensive when rendering explicit surfaces, our proposed method, named RayletDF, introduces a new technique called raylet distance field, which aims to directly predict surface points from query rays. Our pipeline consists of three key modules: a raylet feature extractor, a raylet distance field predictor, and a multi-raylet blender. These components work together to extract fine-grained local geometric features, predict raylet distances, and aggregate multiple predictions to reconstruct precise surface points. We extensively evaluate our method on multiple public real-world datasets, demonstrating superior performance in surface reconstruction from point clouds or 3D Gaussians. Most notably, our method achieves exceptional generalization ability, successfully recovering 3D surfaces in a single-forward pass across unseen datasets in testing.
Abstract (translated)
在这篇论文中，我们提出了一种从RGB图像通过3DGS（假设是某种技术或方法的缩写）重建原始点云或预估的三维高斯分布表面的方法。与现有的基于坐标的那些通常在渲染显式曲面时计算密集型的方法不同，我们的方法命名为RayletDF，引入了名为raylet距离场的新技术，旨在直接从查询光线预测表面上的点。我们的流程包含三个关键模块：一个raylet特征提取器、一个raylet距离场预测器和一个多raylet混合器。这些组件协同工作以抽取精细的局部几何特征，预测raylet距离，并聚合多处预测以重建精确的表面点。我们在多个公共的真实世界数据集上广泛评估了我们的方法，在从点云或三维高斯分布进行曲面重建方面展示了优越的表现。特别值得注意的是，我们的方法在测试时能够通过一次前向传递成功恢复未知数据集中物体的3D表面结构，显示出卓越的一般化能力。
URL
https://arxiv.org/abs/2508.09830
