解决城市驾驶场景中图像重叠少导致的表面重建难题
J-NeuS: Joint field optimization for Neural Surface reconstruction in urban scenes with limited image overlap
- 联合优化两个辐射场并引导采样，提升重建精度
- 在有限视角重叠下仍能准确还原大范围复杂结构
- 适合自动驾驶、城市三维建模等需要高精度重建的场景
从记录的驾驶序列中重建周围表面几何极具挑战性，原因在于城市环境中图像重叠有限且拓扑复杂。现有最先进神经隐式表面重建方法在此类场景中常因视野重叠过小而失败，或在精细结构重建上表现不佳。为此，我们提出 J-NeuS，一种针对外向相机姿态的大规模驾驶序列混合隐式表面重建方法。J-NeuS通过跨表示不确定性估计缓解观测不足带来的几何模糊问题。方法在联合优化两个辐射场的同时引入引导采样，实现复杂城市场景下大范围区域与细粒度结构的精准重建。在主流驾驶数据集上的大量评估表明，该方法在图像重叠受限条件下显著优于当前最先进方法。
原文摘要 · Abstract (English)
Reconstructing the surrounding surface geometry from recorded driving sequences poses a significant challenge due to the limited image overlap and complex topology of urban environments. SoTA neural implicit surface reconstruction methods often struggle in such setting, either failing due to small vision overlap or exhibiting suboptimal performance in accurately reconstructing both the surface and fine structures. To address these limitations, we introduce J-NeuS, a novel hybrid implicit surface reconstruction method for large driving sequences with outward facing camera poses. J-NeuS cross-representation uncertainty estimation to tackle ambiguous geometry caused by limited observations. Our method performs joint optimization of two radiance fields in addition to guided sampling achieving accurate reconstruction of large areas along with fine structures in complex urban scenarios. Extensive evaluation on major driving datasets demonstrates the superiority of our approach in reconstructing large driving sequences with limited image overlap, outperforming concurrent SoTA methods.
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