ObjectSplat：通过物体层级网格溅射提升3D场景的网格保真度与交互性

中文摘要 AI 辅助

基于溅射（splatting）的算法可从普通图像重建出逼真、可实时渲染且可导出网格的3D场景，但这类算法将场景表示为单一整体场，导致重建结果缺乏物体层级结构，无法用于下游编辑或交互。此外，输入扫描中从未直接观测到的区域会被周围纹理污染且无法修正，限制了网格保真度和新视图合成效果。本文提出一种先分解再重建的方法：对每一帧中的实例进行分割，将剩余部分视为背景并进行补全，采用网格溅射分别重建每个实例和背景，再将它们组合成单一场景。该方法显著提升了网格保真度（F值提升超过5%）和新视图合成效果，同时支持物体级别的可修改性与交互性，代码将公开提供。

英文摘要

Splatting-based algorithms reconstruct photorealistic, real-time-renderable, and mesh-exportable 3D scenes from regular images, but they represent a scene as a single monolithic field. Therefore, the reconstruction has no object-level structure, leaving it infeasible for downstream editing or interaction. Moreover, regions that are never directly observed in the input scans are contaminated by the surrounding texture and left uncorrected, capping both mesh fidelity and novel-view synthesis. We propose a decompose-before-reconstruct approach: we segment the instances out of every frame, consider the remaining as background and inpaint it, reconstruct each instance and the background independently with mesh splatting, and compose them into a single scene. Our method significantly improves mesh fidelity (over a 5\% gain in F-score) and novel-view synthesis, while supporting object-wise modifiability and interactivity. The code will be made publicly available.

发表机构

• State University of New York at Albany（纽约州立大学奥尔巴尼分校）

机构由 AI 辅助整理，请以论文原文为准。