Real-Time 3D Scene Perception in Dynamic Urban Environments via Street Detection Gaussians

As a cornerstone for applications such as autonomous driving, 3D urban perception is a burgeoning field of study. Enhancing the performance and robustness of these perception systems is crucial for ensuring the safety of next-generation autonomous vehicles. In this work, we introduce a novel neural scene representation called Street Detection Gaussians (SDGs), which redefines urban 3D perception through an integrated architecture unifying reconstruction and detection. At its core lies the dynamic Gaussian representation, where time-conditioned parameterization enables simultaneous modeling of static environments and dynamic objects through physically constrained Gaussian evolution. The framework’s radar-enhanced perception module learns cross-modal correlations between sparse radar data and dense visual features, resulting in a 22% reduction in occlusion errors compared to vision-only systems. A breakthrough differentiable rendering pipeline back-propagates semantic detection losses throughout the entire 3D reconstruction process, enabling the optimization of both geometric and semantic fidelity. Evaluated on the Waymo Open Dataset and the KITTI Dataset, the system achieves real-time performance (135 Frames Per Second (FPS)), photorealistic quality (Peak Signal-to-Noise Ratio (PSNR) 34.9 dB), and state-of-the-art detection accuracy (78.1% Mean Average Precision (mAP)), demonstrating a $3.8\times$ end-to-end improvement over existing hybrid approaches while enabling seamless integration with autonomous driving stacks.