LiDAR-captured point clouds are often considered the gold standard in active 3D reconstruction. While their accuracy is exceptional in flat regions, the capturing is susceptible to miss small geometric structures and may fail with dark, absorbent materials. Alternatively, capturing multiple photos of the scene and applying 3D photogrammetry can infer these details as they often represent feature-rich regions. However, the accuracy of LiDAR for featureless regions is rarely reached. Therefore, we suggest combining the strengths of LiDAR and camera-based capture by introducing SurfFill: a Gaussian surfel-based LiDAR completion scheme. We analyze LiDAR capturings and attribute LiDAR beam divergence as a main factor for artifacts, manifesting mostly at thin structures and edges. We use this insight to introduce an ambiguity heuristic for completed scans by evaluating the change in density in the point cloud. This allows us to identify points close to missed areas, which we can then use to grow additional points from to complete the scan. For this point growing, we constrain Gaussian surfel reconstruction to focus optimization and densification on these ambiguous areas. Finally, Gaussian primitives of the reconstruction in ambiguous areas are extracted and sampled for points to complete the point cloud. To address the challenges of large-scale reconstruction, we extend this pipeline with a divide-and-conquer scheme for building-sized point cloud completion. We evaluate on the task of LiDAR point cloud completion of synthetic and real-world scenes and find that our method outperforms previous reconstruction methods.

翻译：LiDAR捕获的点云通常被视为主动三维重建中的黄金标准。尽管其在平坦区域具有卓越的精度，但在捕捉过程中容易遗漏微小几何结构，并可能因暗色或吸光材料而失败。相比之下，通过拍摄场景的多张照片并应用三维摄影测量法可以推断这些细节，因为它们通常代表特征丰富的区域。然而，对于无特征区域，摄影测量法难以达到LiDAR的精度。因此，我们提出结合LiDAR和基于相机的捕捉优势，引入SurfFill：一种基于高斯曲面体的LiDAR补全方案。我们分析了LiDAR捕获特性，并将光束发散归因于主要伪影因素，这些伪影在薄结构和边缘处尤为明显。基于此洞察，我们提出一种通过评估点云密度变化来对已完成扫描的模糊性进行启发式判断的方法，从而识别靠近缺失区域的点，并利用这些点生长额外点以补全扫描。在点生长过程中，我们约束高斯曲面体重建，将优化和密化聚焦于这些模糊区域。最终，提取模糊区域重建中的高斯基元并采样得到补全点云的点。为应对大规模重建的挑战，我们将该流程扩展为一种分治策略，用于建筑物规模的点云补全。我们在合成场景和真实场景的LiDAR点云补全任务上进行了评估，结果表明我们的方法优于以往的重建方法。