DinoComplete: 利用蒸馏语义先验和状态空间模型进行3D形状补全
DinoComplete: 3D Shape Completion with Distilled Semantic Priors and State Space Models
AI总结：
提出DinoComplete框架，通过从DINO特征中蒸馏语义先验并结合多尺度体素Mamba模块，实现高效、鲁棒的3D形状补全，在未见类别和真实噪声扫描上优于现有方法。
摘要：
从部分扫描进行3D形状补全对于未见类别和嘈杂的真实世界观测仍然具有挑战性，因为仅凭几何信息往往不足以推断缺失结构。我们提出了DinoComplete，一个确定且高效的形状补全框架，通过从DINO特征中蒸馏的体素对齐语义先验来增强几何重建。首先，我们构建与ShapeNet数据对齐的多视图DINO特征体积，并训练一个学生网络直接从不完整形状预测密集语义特征。这些预测特征捕获全局结构和部分感知的语义上下文，同时与底层几何保持对齐。然后，我们将这些蒸馏特征集成到一个补全网络中，其中几何和语义体素表示通过体素状态空间建模进行融合。为了在不牺牲分辨率的情况下实现高效的长距离推理，我们引入了一个多尺度体素Mamba模块，通过结合全网格和分块序列建模来细化融合特征。在未见过的ShapeNet类别和ScanNet物体上的实验表明，DinoComplete在使用更少参数、更低内存和更快推理速度的同时，实现了比先前确定性和基于生成的方法更强的补全质量。我们的结果表明，从视觉基础模型中蒸馏语义先验提高了3D形状补全的泛化能力和鲁棒性。
英文摘要：
3D shape completion from partial scans remains challenging for unseen categories and noisy real-world observations, where geometry alone is often insufficient for inferring missing structure. We present DinoComplete, a deterministic and efficient shape completion framework that augments geometric reconstruction with voxel-aligned semantic priors distilled from DINO features. First, we construct multi-view DINO feature volumes aligned with ShapeNet data and train a student network to predict dense semantic features directly from incomplete shapes. These predicted features capture global structure and part-aware semantic context while remaining aligned with the underlying geometry. We then integrate these distilled features into a completion network, where geometric and semantic voxel representations are fused through voxel state-space modeling. To enable efficient long-range reasoning without sacrificing resolution, we introduce a multi-scale voxel Mamba module that refines the fused features by combining full-grid and chunk-wise sequence modeling. Experiments on unseen ShapeNet categories and ScanNet objects show that DinoComplete achieves stronger completion quality than prior deterministic and generative based completion methods while using fewer parameters, requiring lower memory, and achieving faster inference. Our results demonstrate that distilling semantic priors from visual foundation models improves generalization and robustness in 3D shape completion.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
