S2D：面向极少输入三维重建的稀疏到稠密提升
S2D: Sparse to Dense Lifting for 3D Reconstruction with Minimal Inputs
用一步扩散模型修复稀疏点云渲染伪影，再以随机采样丢弃和加权梯度策略拟合3DGS，为极少输入视图下的高质量重建提供了可复用的两阶段提升思路。
原文依据 We first present an efficient one-step diffusion model that lifts sparse point cloud for high-fidelity image artifact fixing.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论显式三维表示已成为三维仿真与理解的重要媒介。然而，最常用的点云与三维高斯泼溅（3DGS）各自存在渲染非真实感以及稀疏输入下显著退化的问题。本文提出稀疏到稠密提升（S2D），一种桥接两种表示的新流程，能够在极少输入下实现高质量3DGS重建。具体而言，S2D提升包含两方面：首先，我们提出一种高效的一步扩散模型，用于提升稀疏点云以进行高保真图像伪影修复；同时，为重建三维一致的场景，我们设计了相应的重建策略，采用随机样本丢弃与加权梯度，实现从稀疏输入视图到稠密新视图的鲁棒模型拟合。大量实验表明，S2D在生成新视图引导方面取得最佳一致性，并在不同输入稀疏度下达到第一梯队的稀疏视图重建质量。通过在现有方法中以尽可能少的拍摄重建稳定场景，S2D为3DGS应用实现了最低输入需求。
英文原文摘要 1178 字符
Explicit 3D representations have already become an essential medium for 3D simulation and understanding. However, the most commonly used point cloud and 3D Gaussian Splatting (3DGS) each suffer from non-photorealistic rendering and significant degradation under sparse inputs. In this paper, we introduce Sparse to Dense lifting (S2D), a novel pipeline that bridges the two representations and achieves high-quality 3DGS reconstruction with minimal inputs. Specifically, the S2D lifting is two-fold. We first present an efficient one-step diffusion model that lifts sparse point cloud for high-fidelity image artifact fixing. Meanwhile, to reconstruct 3D consistent scenes, we also design a corresponding reconstruction strategy with random sample drop and weighted gradient for robust model fitting from sparse input views to dense novel views. Extensive experiments show that S2D achieves the best consistency in generating novel view guidance and first-tier sparse view reconstruction quality under different input sparsity. By reconstructing stable scenes with the least possible captures among existing methods, S2D enables minimal input requirements for 3DGS applications.