用于稀疏隐式神经形状补全的观测条件潜在能量先验
Observation-Conditioned Latent Energy Priors for Sparse Implicit Neural Shape Completion
- University of Zurich（苏黎世大学）
- University Hospital Zurich（苏黎世大学医院）
- ETH Zurich（苏黎世联邦理工学院）
- Technical University of Munich（慕尼黑工业大学）
- Munich Center for Machine Learning（慕尼黑机器学习中心）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究针对稀疏隐式神经形状补全中潜在代码漂移问题，提出观测条件潜在能量先验，在两个SDF数据集上验证其可提升预训练INR解码器的观测感知能力与补全性能。
AI中文摘要：
隐式神经表示（INRs）可通过共享坐标解码器和实例级潜在代码对连续3D形状进行建模。在测试时，自解码器风格的模型通常会冻结解码器，并从稀疏的离网SDF样本中优化新的潜在代码。当这些样本对推理的约束不足时，潜在代码会漂移到拟合观测结果但解码出不可信的未观测几何结构的区域。我们提出一种用于冻结INR解码器的事后观测条件潜在能量先验。该能量分数基于稀疏观测集的置换不变编码对潜在代码进行标准化，并与在验证数据上选择的L2潜在先验一起作为残差专家使用。我们在受控的细胞核SDF数据集和源自MedShapeNet的公开SDF补全数据集上进行评估。在最稀疏的细胞核场景中，添加了条件能量的所提L2目标始终优于经验证集选择的L2基线；在MedShapeNet上，其在所有报告的读数上均优于L2和六分量GMM潜在密度先验。打乱上下文的消融实验效果始终弱于匹配上下文，这支持了观测特定贡献的存在。这些结果表明，轻量级条件能量可使预训练的INR解码器更具观测感知能力，而无需重新训练。
英文摘要：
Implicit neural representations (INRs) can model continuous 3D shapes with a shared coordinate decoder and per-instance latent codes. At test time, autodecoder-style models commonly freeze the decoder and optimize a new latent code from sparse off-grid SDF samples. When these samples underconstrain inference, the latent can drift toward regions that fit the observations but decode implausible unobserved geometry. We propose a post-hoc observation-conditioned latent energy prior for frozen INR decoders. The energy scores standardized latents conditioned on a permutation-invariant encoding of the sparse observation set and is used as a residual expert alongside an L2 latent prior selected on validation data. We evaluate on a controlled cell-nucleus SDF dataset and a public MedShapeNet-derived SDF completion dataset. The proposed L2 objective augmented with conditional energy improves consistently over a validation-selected L2 baseline in the sparsest cell-nucleus regimes and, on MedShapeNet, outperforms both L2 and a six-component GMM latent-density prior across all reported readouts. A shuffled-context ablation is consistently weaker than matched context, supporting an observation-specific contribution. These results suggest that lightweight conditional energies can make pretrained INR decoders more observation-aware without retraining.
