用元学习让NeRF快速认出物体类别，重建更准更快。
Category-level Meta-learned NeRF Priors for Efficient Object Mapping
- 通过元学习合成数据训练，让NeRF学会识别物体类别特征。
- 在真实数据上比无先验方法低13%重建误差，训练时间减少5倍。
- 适合需要快速高精度3D重建的机器人、AR应用开发者。
在3D物体映射中，类别级先验可实现高效物体重建与规范姿态估计，只需每类一个先验（如椅子、书本、笔记本）。目前主流使用DeepSDF作为类别级形状先验，但难以还原精细几何且计算开销大。相比之下，NeRF能捕捉细节，却尚未有效融入类别级先验以支持实时多物体映射。为此，本文提出PRENOM——基于先验的高效神经物体映射器，将类别级先验与物体级NeRF结合，提升重建效率并支持规范姿态估计。PRENOM通过在开源形状数据集生成的合成重建任务上进行元学习，实现对物体的“第一印象”式认知。针对类别差异，采用多目标遗传算法优化每类的NeRF架构，在重建质量与训练时间间取得平衡。此外，基于先验的概率射线采样策略引导采样至预期物体区域，加速收敛并提升资源受限下的重建质量。实验表明，PRENOM在合成数据上相比无先验的NeRF方法，Chamfer距离降低21%；在噪声真实数据上，平均重建指标提升13%，姿态与尺寸估计精度相当，但训练时间仅为其他方法的1/5。代码已公开：https://github.com/snt-arg/PRENOM
原文摘要 · Abstract (English)
In 3D object mapping, category-level priors enable efficient object reconstruction and canonical pose estimation, requiring only a single prior per semantic category (e.g., chair, book, laptop, etc.). DeepSDF has been used predominantly as a category-level shape prior, but it struggles to reconstruct sharp geometry and is computationally expensive. In contrast, NeRFs capture fine details but have yet to be effectively integrated with category-level priors in a real-time multi-object mapping framework. To bridge this gap, we introduce PRENOM, a Prior-based Efficient Neural Object Mapper that integrates category-level priors with object-level NeRFs to enhance reconstruction efficiency and enable canonical object pose estimation. PRENOM gets to know objects on a first-name basis by meta-learning on synthetic reconstruction tasks generated from open-source shape datasets. To account for object category variations, it employs a multi-objective genetic algorithm to optimize the NeRF architecture for each category, balancing reconstruction quality and training time. Additionally, prior-based probabilistic ray sampling directs sampling toward expected object regions, accelerating convergence and improving reconstruction quality under constrained resources. Experimental results highlight the ability of PRENOM to achieve high-quality reconstructions while maintaining computational feasibility. Specifically, comparisons with prior-free NeRF-based approaches on a synthetic dataset show a 21\% lower Chamfer distance. Furthermore, evaluations against other approaches using shape priors on a noisy real-world dataset indicate a 13\% improvement averaged across all reconstruction metrics, and comparable pose and size estimation accuracy, while being trained for 5$\times$ less time. Code available at: https://github.com/snt-arg/PRENOM
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