IGFuse：通过多次扫描融合实现交互式三维高斯场景重建
IGFuse: Interactive 3D Gaussian Scene Reconstruction via Multi-Scans Fusion
AI总结：
IGFuse 通过融合多次扫描中因物体重新摆放而揭示的遮挡区域，构建分割感知高斯场并施加双向一致性与协同共剪枝，实现无需密集观测的高保真交互式三维场景重建与物体级操控。
摘要：
重建完整且可交互的三维场景仍然是计算机视觉和机器人领域的根本性挑战，尤其是由于持续存在的物体遮挡和有限的传感器覆盖范围。来自单次场景扫描的多视角观测往往无法捕获完整的结构细节。现有方法通常依赖多阶段流程，例如分割、背景补全和图像修复，或者需要对每个物体进行密集扫描，这两种方式都容易出错且不易扩展。我们提出 IGFuse，一种新颖的框架，通过融合来自多次扫描的观测来重建交互式高斯场景，其中扫描之间自然的物体重新摆放会揭示先前被遮挡的区域。我们的方法构建了具有分割感知的高斯场，并在各次扫描之间强制执行双向光度一致性和语义一致性。为了处理空间错位，我们引入了一个伪中间场景状态用于统一对齐，同时采用协同共剪枝策略来优化几何结构。IGFuse 无需密集观测或复杂流程即可实现高保真渲染和物体级场景操控。大量实验验证了该框架对新颖场景配置的强大泛化能力，证明了其在真实世界三维重建和真实到仿真迁移中的有效性。我们的项目页面已在线发布。
英文摘要：
Reconstructing complete and interactive 3D scenes remains a fundamental challenge in computer vision and robotics, particularly due to persistent object occlusions and limited sensor coverage. Multiview observations from a single scene scan often fail to capture the full structural details. Existing approaches typically rely on multi stage pipelines, such as segmentation, background completion, and inpainting or require per-object dense scanning, both of which are error-prone, and not easily scalable. We propose IGFuse, a novel framework that reconstructs interactive Gaussian scene by fusing observations from multiple scans, where natural object rearrangement between captures reveal previously occluded regions. Our method constructs segmentation aware Gaussian fields and enforces bi-directional photometric and semantic consistency across scans. To handle spatial misalignments, we introduce a pseudo-intermediate scene state for unified alignment, alongside collaborative co-pruning strategies to refine geometry. IGFuse enables high fidelity rendering and object level scene manipulation without dense observations or complex pipelines. Extensive experiments validate the framework's strong generalization to novel scene configurations, demonstrating its effectiveness for real world 3D reconstruction and real-to-simulation transfer. Our project page is available online.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
