EMIE-MAP: Large-Scale Road Surface Reconstruction Based on Explicit Mesh
  and Implicit Encoding
Wenhua Wu Qi Wang Guangming Wang Junping Wang Tiankun Zhao Yang Liu Dongchao Gao Zhe Liu Hesheng Wang
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.