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arXiv Machine Learning By Shi Chen, Weifeng Ge

Generative Semantic Scene Completion

Read the original on arXiv Machine Learning →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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