提出高效精准的鸟瞰图道路重建方法，解决视图转换信息丢失问题。
FastRSR: Efficient and Accurate Road Surface Reconstruction from Bird's Eye View
- 用深度感知投影实现无损鸟瞰图转换，减少特征稀疏性。
- 在RSRD数据集上，单目误差降低6.0%，立体匹配速度提升3倍以上。
- 适合自动驾驶中实时高精度道路建模场景使用。
道路表面重建（RSR）对自动驾驶至关重要，有助于理解路面状况。近年来，基于鸟瞰图（BEV）的RSR因其性能提升潜力受到关注。然而，现有从透视图到鸟瞰图的转换方法存在信息丢失和表征稀疏等问题，且鸟瞰图下的立体匹配难以兼顾精度与推理速度。为此，本文提出两种高效精准的基于鸟瞰图的RSR模型：FastRSR-mono与FastRSR-stereo。首先引入深度感知投影（DAP），通过查询深度与图像特征，利用预计算的查找表在特定路面区域聚合鸟瞰图数据，有效缓解信息损失与稀疏性。为优化立体匹配中的精度与速度，设计空间注意力增强（SAE）与置信度注意力生成（CAG）模块：SAE自适应突出关键区域，CAG聚焦高置信度预测并过滤无关信息。FastRSR在RSRD数据集上达到当前最优性能，相比单目方法降低超过6.0%的高程绝对误差，立体方法推理速度提升至少3.0倍。代码将公开。
原文摘要 · Abstract (English)
Road Surface Reconstruction (RSR) is crucial for autonomous driving, enabling the understanding of road surface conditions. Recently, RSR from the Bird's Eye View (BEV) has gained attention for its potential to enhance performance. However, existing methods for transforming perspective views to BEV face challenges such as information loss and representation sparsity. Moreover, stereo matching in BEV is limited by the need to balance accuracy with inference speed. To address these challenges, we propose two efficient and accurate BEV-based RSR models: FastRSR-mono and FastRSR-stereo. Specifically, we first introduce Depth-Aware Projection (DAP), an efficient view transformation strategy designed to mitigate information loss and sparsity by querying depth and image features to aggregate BEV data within specific road surface regions using a pre-computed look-up table. To optimize accuracy and speed in stereo matching, we design the Spatial Attention Enhancement (SAE) and Confidence Attention Generation (CAG) modules. SAE adaptively highlights important regions, while CAG focuses on high-confidence predictions and filters out irrelevant information. FastRSR achieves state-of-the-art performance, exceeding monocular competitors by over 6.0% in elevation absolute error and providing at least a 3.0x speedup by stereo methods on the RSRD dataset. The source code will be released.
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