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.

本文介绍了一种新颖的框架实施几何密集束调整（DBA），使用 3D 神经隐式表面进行地图参数化，通过密集光流预测引导的几何误差来优化地图表面和轨迹姿态，进一步改进了密集 * 映射的质量。我们在多个行驶场景数据集上的实验结果表明，我们的方法在轨迹优化和密集重建精度方面取得了优越的效果。我们还研究了光度误差和不同的神经几何先验对表面重建和新视角合成性能的影响。我们的方法是利用神经隐式表示在密集束调整中实现更准确的轨迹和详细环境映射的重要一步。