Abstract
Road surface reconstruction plays a vital role in autonomous driving systems, enabling road lane perception and high-precision mapping. Recently, neural implicit encoding has achieved remarkable results in scene representation, particularly in the realistic rendering of scene textures. However, it faces challenges in directly representing geometric information for large-scale scenes. To address this, we propose EMIE-MAP, a novel method for large-scale road surface reconstruction based on explicit mesh and implicit encoding. The road geometry is represented using explicit mesh, where each vertex stores implicit encoding representing the color and semantic information. To overcome the difficulty in optimizing road elevation, we introduce a trajectory-based elevation initialization and an elevation residual learning method based on Multi-Layer Perceptron (MLP). Additionally, by employing implicit encoding and multi-camera color MLPs decoding, we achieve separate modeling of scene physical properties and camera characteristics, allowing surround-view reconstruction compatible with different camera models. Our method achieves remarkable road surface reconstruction performance in a variety of real-world challenging scenarios.
Abstract (translated)
道路表面重构在自动驾驶系统中起着至关重要的作用，实现了道路车道的感知和精确地图绘制。最近，神经隐式编码在场景表示方面取得了显著的成果，特别是在真实感绘制场景纹理方面。然而，在直接表示大规模场景的几何信息方面，它面临挑战。为了应对这个问题，我们提出了EMIE-MAP，一种基于显式网格和隐式编码的大型道路表面重构新方法。道路几何信息通过显式网格表示，其中每个顶点存储隐式编码，表示颜色和语义信息。为了克服优化道路抬升的困难，我们引入了基于轨迹的抬升初始化和基于多层感知器（MLP）的抬升残差学习方法。此外，通过采用隐式编码和多相机颜色MLP解码，我们实现了场景物理特性和相机特性的单独建模，允许不同相机模型的环绕视图重建。我们的方法在各种现实世界的具有挑战性的场景中取得了出色的道路表面重构性能。
URL
https://arxiv.org/abs/2403.11789
