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New GSSC method advances LiDAR semantic scene completion using discrete diffusion

By PulseAugur Editorial · [3 sources] ·

Researchers have introduced Generative Semantic Scene Completion (GSSC), a novel approach to reconstructing dense semantic voxel grids from sparse LiDAR scans. This method utilizes a single discrete-diffusion formulation with three distinct roles: PS$^3$ for generating synthetic sparse-dense scene pairs, SGSC for generating scenes from noise conditioned on sparse data, and S$^2$D$^2$ for refining existing completions. The S$^2$D$^2$ component notably improves performance on the SemanticKITTI benchmark, achieving state-of-the-art results. AI

IMPACT Advances semantic scene completion capabilities, potentially improving autonomous navigation and 3D scene understanding.

RANK_REASON The cluster describes a new method and dataset for a computer vision task, presented in an academic paper.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

New GSSC method advances LiDAR semantic scene completion using discrete diffusion

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COVERAGE [3]

  1. arXiv cs.LG TIER_1 English(EN) · Shi Chen, Weifeng Ge ·

    Generative Semantic Scene Completion

    arXiv:2608.26737v1 Announce Type: cross Abstract: Outdoor LiDAR semantic scene completion (SSC) recovers a dense semantic voxel grid from a scan observing 1% of the target volume, under class imbalance beyond 7,000x. We recast SSC as generative semantic scene completion (GSSC): a…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    Generative Semantic Scene Completion

    Outdoor LiDAR semantic scene completion (SSC) recovers a dense semantic voxel grid from a scan observing 1% of the target volume, under class imbalance beyond 7,000x. We recast SSC as generative semantic scene completion (GSSC): a single discrete-diffusion formulation in three ro…

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    Generative Semantic Scene Completion

    Generative discrete diffusion recasts outdoor LiDAR semantic scene completion via paired sparse-dense synthesis, semantic-guided generation, and structured refinement to achieve state-of-the-art results.

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