arXiv:2607. 26645v1 Announce Type: cross Abstract: Existing point-based generative methods for outdoor scenes primarily focus on LiDAR-conditioned completion.
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arXiv:2609.24825v1 Announce Type: new Abstract: LiDAR point clouds acquired in underground environments exhibit severe geometric incompleteness due to occlusions and limited sensor viewpoints, making...
arXiv:2609.17413v1 Announce Type: new Abstract: This paper investigates easy strategies to boost the performance of existing networks for lidar semantic scene completion (SSC) without requiring compl...
LiDAR point clouds acquired in underground environments exhibit severe geometric incompleteness due to occlusions and limited sensor viewpoints, making reliable point cloud completion challenging with...
arXiv:2609.15228v1 Announce Type: new Abstract: Unsupervised registration of large-scale LiDAR point clouds remains challenging due to the geometric ambiguity inherent in outdoor scenes, which degrad...
arXiv:2404.09431v3 Announce Type: replace Abstract: Pseudo-LiDAR has become a promising paradigm for monocular 3D object detection by transforming monocular images into point cloud representations th...
The paper introduces Generative Semantic Scene Completion (GSSC), a framework that recasts outdoor LiDAR semantic scene completion as a discrete diffusion process. It comprises three components: (1) paired sparse‑dense scene synthesis (PS³) to generate synthetic training data, (2) semantic‑guided generative scene completion (SGSC) that generates scenes from noise conditioned on sparse scans, and (3) structured source discrete diffusion (S²D²) that refines existing completions in a single flow‑matching step. Using this approach, the authors achieve state‑of‑the‑art performance on the SemanticKITTI benchmark, reaching 38.8% mIoU in a single‑sweep, single‑sample setting and 39.2% with limited augmentation.