发表机构
Nanjing University; Nankai University（南京大学; 南开大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出3DTexMOR方法，通过在多视图共享的纹理空间中修复，结合外观分解与几何正则化，实现了最优的三维多物体移除性能，PSNR提升5.8dB、LPIPS至少降低22%。
AI 中文摘要
三维物体移除旨在从重建场景中移除目标物体，并补全被遮挡区域的几何结构与外观。现有基于神经辐射场（NeRF）和三维高斯溅射（3DGS）的方法通常通过修复二维图像来引导三维补全，但复杂的多物体布局会限制每个视图中可见的周围上下文，导致二维修复易产生伪影；不同视图间的补全结果不一致还会引入冲突的监督信号，造成重建结果模糊。本文提出3DTexMOR（基于纹理空间修复的三维高斯多物体移除），核心思路是在所有视图共享的统一纹理空间中进行修复，该空间通过融合互补观测信息，为缺失区域的恢复提供更丰富的上下文，并促进跨视图外观一致性。具体而言，本文将多视图观测聚合为纹理图，对缺失区域进行修复，再将补全后的图重投影到相机视图，以监督高斯场景补全；为避免视图相关的高光与反射的影响，本文分解外观信息，聚合与视图无关的内在属性而非RGB颜色；还引入几何正则化的高斯补全，以约束补全区域的几何结构。大量实验表明，该方法能生成视觉上合理的补全结果，实现了多物体移除的最优性能，与现有方法相比，峰值信噪比（PSNR）提升了5.8分贝，学习感知图像块相似度（LPIPS）至少降低了22%。
英文摘要
3D object removal aims to remove target objects from reconstructed scenes and complete the geometry and appearance of occluded regions. Existing NeRF- and 3DGS-based methods typically inpaint 2D images to guide 3D completion. However, complex multi-object layouts limit the surrounding context visible in each view, making 2D inpainting prone to artifacts. Inconsistent completions across views also introduce conflicting supervision and blurry reconstructions. We propose 3D Gaussian Multi-Object Removal via Texture-Space Inpainting (3DTexMOR). Our key idea is to perform inpainting in a unified texture space shared by all views. By combining complementary observations, this space provides richer context for recovering missing regions and promotes cross-view appearance consistency. We aggregate multi-view observations into texture maps, inpaint the missing regions, and reproject the completed maps into camera views to supervise Gaussian scene completion. To avoid the influence of view-dependent highlights and reflections, we decompose appearance and aggregate view-independent intrinsic attributes instead of RGB colors. We further introduce geometrically regularized Gaussian completion to constrain the geometry of the completed regions. Extensive experiments demonstrate visually plausible completions and state-of-the-art multi-object removal performance, improving PSNR by 5.8 dB and reducing LPIPS by at least 22% compared with existing methods.
Comments17 pages, including appendix. Kunxin Guang and Yonghao Zhao contributed equally
