Abstract
3D reconstruction and novel view synthesis are critical for validating autonomous driving systems and training advanced perception models. Recent self-supervised methods have gained significant attention due to their cost-effectiveness and enhanced generalization in scenarios where annotated bounding boxes are unavailable. However, existing approaches, which often rely on frequency-domain decoupling or optical flow, struggle to accurately reconstruct dynamic objects due to imprecise motion estimation and weak temporal consistency, resulting in incomplete or distorted representations of dynamic scene elements. To address these challenges, we propose 4DRadar-GS, a 4D Radar-augmented self-supervised 3D reconstruction framework tailored for dynamic driving scenes. Specifically, we first present a 4D Radar-assisted Gaussian initialization scheme that leverages 4D Radar's velocity and spatial information to segment dynamic objects and recover monocular depth scale, generating accurate Gaussian point representations. In addition, we propose a Velocity-guided PointTrack (VGPT) model, which is jointly trained with the reconstruction pipeline under scene flow supervision, to track fine-grained dynamic trajectories and construct temporally consistent representations. Evaluated on the OmniHD-Scenes dataset, 4DRadar-GS achieves state-of-the-art performance in dynamic driving scene 3D reconstruction.
Abstract (translated)
3D重建和新视角合成对于验证自动驾驶系统以及训练高级感知模型至关重要。最近的自监督方法由于其成本效益高且在缺乏标注边界框场景中的泛化能力增强，受到了广泛关注。然而，现有的大多数依赖频域解耦或光流的方法，在动态对象重建时往往因运动估计不精确和时间一致性弱而导致动态场景元素的表示不完整或扭曲。 为解决这些挑战，我们提出了4DRadar-GS框架，这是一个针对动态驾驶场景设计的、利用4D雷达增强的自监督3D重建方法。具体而言，首先我们提出了一种4D雷达辅助高斯初始化方案，该方案利用了4D雷达的速度和空间信息来分割动态对象并恢复单目深度尺度，从而生成精确的高斯点表示。 此外，我们还提出了一个速度引导的点追踪（Velocity-guided PointTrack，VGPT）模型。此模型与重建管道一起在场景流监督下联合训练，以跟踪精细的动态轨迹，并构造时间上一致的表示形式。 我们在OmniHD-Scenes数据集上对该方法进行了评估，结果表明4DRadar-GS在动态驾驶场景3D重建方面达到了最先进的性能水平。
URL
https://arxiv.org/abs/2509.12931
