Large-Scale Point Cloud Completion Through Registration and Fusion of Object-Level Reconstructions

Existing 3D reconstruction algorithms commonly struggle with modeling specific local objects within large-scale scenes, often resulting in a lack of local detail and incomplete geometric structures. While current mainstream point cloud completion methods can restore these missing structures to some degree, they are fundamentally based on generative in-filling, a process that relies on geometric priors learned from large-scale datasets. Consequently, the physical realism and geometric accuracy of the results cannot be guaranteed. To address these limitations, this paper proposes a novel, data-driven framework for point cloud completion. Our core method involves the high-precision, heterogeneous data registration and seamless fusion of an object-level point cloud—reconstructed with high-fidelity appearance and geometry by our optimized Neural Radiance Fields (NeRF) framework—with our target large-scale scene point cloud. By using high-precision, physically based data as a strong prior for geometric completion, we offer an alternative route to conventional generative completion methods. Concurrently, we employ unsupervised evaluation metrics to assess the intrinsic quality of the final results. This work provides a robust and high-fidelity solution to the problem of completing local objects within large-scale scenes. Evaluated on our self-constructed UAV-Recon dataset, the proposed method achieved a Structural Plausibility ≥ 0.995, Geometric Smoothness ≤ 0.19, and Distribution Uniformity ≈ 1.2, offering a robust solution for the high-fidelity completion of local objects within large-scale scenes.