The joint optimization of the sensor trajectory and 3D map is a crucial characteristic of bundle adjustment (BA), essential for autonomous driving. This paper presents $\nu$-DBA, a novel framework implementing geometric dense bundle adjustment (DBA) using 3D neural implicit surfaces for map parametrization, which optimizes both the map surface and trajectory poses using geometric error guided by dense optical flow prediction. Additionally, we fine-tune the optical flow model with per-scene self-supervision to further improve the quality of the dense mapping. Our experimental results on multiple driving scene datasets demonstrate that our method achieves superior trajectory optimization and dense reconstruction accuracy. We also investigate the influences of photometric error and different neural geometric priors on the performance of surface reconstruction and novel view synthesis. Our method stands as a significant step towards leveraging neural implicit representations in dense bundle adjustment for more accurate trajectories and detailed environmental mapping.

翻译：光束法平差中传感器轨迹与三维地图的联合优化是其关键特性，对自动驾驶至关重要。本文提出$ν$-DBA框架，该框架利用三维神经隐式表面进行地图参数化，通过稠密光流预测引导的几何误差同时优化地图表面与轨迹位姿。此外，我们采用逐场景自监督方法微调光流模型，进一步提升稠密建图质量。在多个驾驶场景数据集上的实验结果表明，本方法在轨迹优化与稠密重建精度方面均取得优越性能。我们还研究了光度误差及不同神经几何先验对表面重建与新视角合成表现的影响。本方法标志着在稠密光束法平差中利用神经隐式表示实现更精确轨迹与更精细环境建图的重要进展。