模糊LiDAR实现更清晰3D：基于漫射LiDAR与RGB的鲁棒手持3D扫描
Blurred LiDAR for Sharper 3D: Robust Handheld 3D Scanning with Diffuse LiDAR and RGB
AI总结：
针对RGB重建在复杂场景的缺陷及稀疏LiDAR的场景覆盖问题，本研究提出漫射LiDAR结合RGB的方法，通过高斯表面元渲染框架及场景自适应损失函数，实现更优的鲁棒3D扫描。
摘要：
3D表面重建在虚拟现实、机器人和移动扫描等应用中至关重要。然而，基于RGB的重建在低纹理、低光照和低反照率场景中常失败。手持LiDAR如今在移动设备上很常见，旨在通过对投影点粗网格的飞行时间测量捕获深度信息来应对这些挑战。但这些稀疏LiDAR在有限输入视图下难以覆盖场景，导致深度信息存在大缺口。本研究提出使用“模糊”LiDAR的替代类别，其发射漫射闪光，大幅提升场景覆盖度，但因宽视场内混合飞行时间测量引入空间模糊性。为处理这些模糊性，我们提出利用漫射LiDAR与RGB的互补优势，引入基于高斯表面元的渲染框架，该框架带有场景自适应损失函数，可动态平衡RGB和漫射LiDAR信号。我们证明，漫射LiDAR能出人意料地优于传统稀疏LiDAR，在挑战性环境中实现具有准确颜色和几何估计的鲁棒3D扫描。
英文摘要：
3D surface reconstruction is essential across applications of virtual reality, robotics, and mobile scanning. However, RGB-based reconstruction often fails in low-texture, low-light, and low-albedo scenes. Handheld LiDARs, now common on mobile devices, aim to address these challenges by capturing depth information from time-of-flight measurements of a coarse grid of projected dots. Yet, these sparse LiDARs struggle with scene coverage on limited input views, leaving large gaps in depth information. In this work, we propose using an alternative class of "blurred" LiDAR that emits a diffuse flash, greatly improving scene coverage but introducing spatial ambiguity from mixed time-of-flight measurements across a wide field of view. To handle these ambiguities, we propose leveraging the complementary strengths of diffuse LiDAR with RGB. We introduce a Gaussian surfel-based rendering framework with a scene-adaptive loss function that dynamically balances RGB and diffuse LiDAR signals. We demonstrate that, surprisingly, diffuse LiDAR can outperform traditional sparse LiDAR, enabling robust 3D scanning with accurate color and geometry estimation in challenging environments.
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。
