3D object-level mapping is a fundamental problem in robotics, which is especially challenging when object CAD models are unavailable during inference. In this work, we propose a framework that can reconstruct high-quality object-level maps for unknown objects. Our approach takes multiple RGB-D images as input and outputs dense 3D shapes and 9-DoF poses (including 3 scale parameters) for detected objects. The core idea of our approach is to leverage a learnt generative model for shape categories as a prior and to formulate a probabilistic, uncertainty-aware optimization framework for 3D reconstruction. We derive a probabilistic formulation that propagates shape and pose uncertainty through two novel loss functions. Unlike current state-of-the-art approaches, we explicitly model the uncertainty of the object shapes and poses during our optimization, resulting in a high-quality object-level mapping system. Moreover, the resulting shape and pose uncertainties, which we demonstrate can accurately reflect the true errors of our object maps, can also be useful for downstream robotics tasks such as active vision. We perform extensive evaluations on indoor and outdoor real-world datasets, achieving achieves substantial improvements over state-of-the-art methods. Our code will be available at https://github.com/TRAILab/UncertainShapePose.

翻译：三维物体级建图是机器人领域的基础问题，当推理过程中无法获取物体CAD模型时尤为具有挑战性。本文提出一种面向未知物体的高质量物体级建图框架。该方法以多张RGB-D图像为输入，输出检测物体的稠密三维形状及9自由度位姿（含3个尺度参数）。其核心思想在于将学习得到的形状类别生成模型作为先验，并构建概率化、不确定性感知的优化框架进行三维重建。我们推导出概率化建模方法，通过两种新颖的损失函数传播形状与位姿的不确定性。与当前主流方法不同，我们在优化过程中显式建模物体形状与位姿的不确定性，最终形成高质量的物体级建图系统。此外，本文证明所生成的形状与位姿不确定性可准确反映物体地图的真实误差，并可应用于主动视觉等下游机器人任务。在室内外真实数据集上的大量实验表明，本方法较现有技术取得显著提升。代码将开源于https://github.com/TRAILab/UncertainShapePose。