Constructing 3D representations of object geometry is critical for many downstream manipulation tasks. These representations must be built from potentially noisy partial observations. In this work we focus on the problem of reconstructing a multi-object scene from a single RGBD image. Current deep learning approaches to this problem can be brittle to noisy real world observations and out-of-distribution objects. Other approaches that do not rely on training data cannot accurately infer the backside of objects. We propose BRRP, a reconstruction method that can leverage preexisting mesh datasets to build an informative prior during robust probabilistic reconstruction. In order to make our method more efficient, we introduce the concept of retrieval-augmented prior, where we retrieve relevant components of our prior distribution during inference. Our method produces a distribution over object shape that can be used for reconstruction or measuring uncertainty. We evaluate our method in both procedurally generated scenes and in real world scenes. We show our method is more robust than a deep learning approach while being more accurate than a method with an uninformative prior.

翻译：构建物体几何的三维表征对于许多下游操作任务至关重要。这些表征必须从可能存在噪声的部分观测中构建。本工作聚焦于从单幅RGBD图像重建多物体场景的问题。当前针对该问题的深度学习方法对真实世界观测噪声和分布外物体较为脆弱。其他不依赖训练数据的方法则无法准确推断物体的背面几何。我们提出BRRP方法，一种能够在鲁棒概率重建过程中利用现有网格数据集构建信息性先验的重建方法。为提升方法效率，我们引入检索增强先验的概念，在推理过程中动态检索先验分布的相关组件。本方法生成物体形状的概率分布，可用于重建或不确定性度量。我们在程序生成场景和真实场景中评估了本方法，结果表明：相较于深度学习方法，本方法具有更强的鲁棒性；相较于使用无信息先验的方法，本方法具有更高的准确性。