Reconstructing 3D vehicles from noisy and sparse partial point clouds is of
great significance to autonomous driving. Most existing 3D reconstruction
methods cannot be directly applied to this problem because they are elaborately
designed to deal with dense inputs with trivial noise. In this work, we propose
a novel framework, dubbed MV-DeepSDF, which estimates the optimal Signed
Distance Function (SDF) shape representation from multi-sweep point clouds to
reconstruct vehicles in the wild. Although there have been some SDF-based
implicit modeling methods, they only focus on single-view-based reconstruction,
resulting in low fidelity. In contrast, we first analyze multi-sweep
consistency and complementarity in the latent feature space and propose to
transform the implicit space shape estimation problem into an element-to-set
feature extraction problem. Then, we devise a new architecture to extract
individual element-level representations and aggregate them to generate a
set-level predicted latent code. This set-level latent code is an expression of
the optimal 3D shape in the implicit space, and can be subsequently decoded to
a continuous SDF of the vehicle. In this way, our approach learns consistent
and complementary information among multi-sweeps for 3D vehicle reconstruction.
We conduct thorough experiments on two real-world autonomous driving datasets
(Waymo and KITTI) to demonstrate the superiority of our approach over
state-of-the-art alternative methods both qualitatively and quantitatively.

从嘈杂和稀疏的部分点云中重建 3D 汽车对于自动驾驶非常重要。我们提出了一种名为 MV-DeepSDF 的新框架，通过估计多次扫描点云中的最优符号距离函数（SDF）形状表示来重建野外汽车。通过在潜在特征空间中分析多次扫描的一致性和互补性，并将隐式空间形状估计问题转化为元素到集合特征提取问题，我们首次探讨了多次扫描的重建。通过设计一种新的架构来提取单个元素级表示并将它们聚合生成集合级的预测潜在代码，得到隐式空间中最优 3D 形状的表达，并可以随后解码为连续的 SDF 汽车。通过对两个实际自动驾驶数据集（Waymo 和 KITTI）进行彻底实验，我们展示了我们的方法在质量和量化方面优于现有的替代方法。