论文标题
基于能量的残留潜在运输，无监督点云完成
Energy-Based Residual Latent Transport for Unsupervised Point Cloud Completion
论文作者
论文摘要
无监督的点云完成旨在推断部分对象观察的整个几何形状，而无需部分完整的对应关系。与现有的确定性方法不同，我们主张基于生成建模的无监督点云完成，以探索缺失的对应关系。具体而言，我们提出了一个新颖的框架，该框架通过使用潜在传输模块转换为完整的部分形状来完成完成，并且它被设计为编码器 - 编码器体系结构中的潜在空间基于基于空间的能量模型（EBM），旨在学习以部分形式编码的概率分布。为了训练潜在的代码传输模块和编码器 - 模块网络共同介绍了一个残留采样策略，其中残留捕获了部分和完整形状的潜在空间之间的域间隙。作为一种基于生成模型的框架，我们的方法可以产生与人类感知一致的不确定性图，从而导致无法解释的无监督点云完成。我们通过实验表明，所提出的方法产生高保真的完成结果，超过了最先进的模型。
Unsupervised point cloud completion aims to infer the whole geometry of a partial object observation without requiring partial-complete correspondence. Differing from existing deterministic approaches, we advocate generative modeling based unsupervised point cloud completion to explore the missing correspondence. Specifically, we propose a novel framework that performs completion by transforming a partial shape encoding into a complete one using a latent transport module, and it is designed as a latent-space energy-based model (EBM) in an encoder-decoder architecture, aiming to learn a probability distribution conditioned on the partial shape encoding. To train the latent code transport module and the encoder-decoder network jointly, we introduce a residual sampling strategy, where the residual captures the domain gap between partial and complete shape latent spaces. As a generative model-based framework, our method can produce uncertainty maps consistent with human perception, leading to explainable unsupervised point cloud completion. We experimentally show that the proposed method produces high-fidelity completion results, outperforming state-of-the-art models by a significant margin.
