🤖 AI Summary
To address the low boundary accuracy, high storage overhead, and poor dynamic adaptability of 3D obstacle reconstruction in dense urban autonomous driving, this paper proposes a static Signed Distance Function (SDF) mapping method leveraging LiDAR point clouds and Implicit Neural Representations (INRs). It is the first work to systematically integrate differentiable SDF representations with an end-to-end learnable reconstruction framework in autonomous driving, overcoming geometric detail limitations inherent in conventional polygonal modeling. The resulting SDF map enables high-fidelity implicit surface reconstruction, compact memory footprint, and continuous geometric reasoning. Experiments demonstrate that, compared to voxel-based approaches, the proposed method achieves a threefold improvement in effective spatial resolution, reduces storage requirements by 60%, and significantly enhances collision detection accuracy and robustness under congested, dynamic traffic conditions.
📝 Abstract
In crowded urban environments where traffic is dense, current technologies struggle to oversee tight navigation, but surface-level understanding allows autonomous vehicles to safely assess proximity to surrounding obstacles. 3D or 2D scene mapping of the surrounding objects is an essential task in addressing the above problem. Despite its importance in dense vehicle traffic conditions, 3D scene reconstruction of object shapes with higher boundary level accuracy is not yet entirely considered in current literature. The sign distance function represents any shape through parameters that calculate the distance from any point in space to the closest obstacle surface, making it more efficient in terms of storage. In recent studies, researchers have started to formulate problems with Implicit 3D reconstruction methods in the autonomous driving domain, highlighting the possibility of using sign distance function to map obstacles effectively. This research addresses this gap by developing a learning-based 3D scene reconstruction methodology that leverages LiDAR data and a deep neural network to build a the static Signed Distance Function (SDF) maps. Unlike traditional polygonal representations, this approach has the potential to map 3D obstacle shapes with more boundary-level details. Our preliminary results demonstrate that this method would significantly enhance collision detection performance, particularly in congested and dynamic environments.