In recent years, neural distance functions trained via volumetric ray
marching have been widely adopted for multi-view 3D reconstruction. These
methods, however, apply the ray marching procedure for the entire scene volume,
leading to reduced sampling efficiency and, as a result, lower reconstruction
quality in the areas of high-frequency details. In this work, we address this
problem via joint training of the implicit function and our new coarse
sphere-based surface reconstruction. We use the coarse representation to
efficiently exclude the empty volume of the scene from the volumetric ray
marching procedure without additional forward passes of the neural surface
network, which leads to an increased fidelity of the reconstructions compared
to the base systems. We evaluate our approach by incorporating it into the
training procedures of several implicit surface modeling methods and observe
uniform improvements across both synthetic and real-world datasets. Our
codebase can be accessed via the project page:
this https URL

本文提出通过联合训练隐式函数和新的粗球面基础表面重构方法解决多视角三维重建中高频细节重建效率低下的问题，并将其应用到多种隐式表面建模方法的训练过程中，从而获得在合成数据和实际数据集上的统一改进。

神经隐式曲面的球形引导训练

Sphere-Guided Training of Neural Implicit Surfaces

Pothole detection is one of the most important tasks for road maintenance.
Computer vision approaches are generally based on either 2D road image analysis
or 3D road surface modeling. However, these two categories are always used
independently. Furthermore, the pothole detection accuracy is still far from
satisfactory. Therefore, in this paper, we present a robust pothole detection
algorithm that is both accurate and computationally efficient. A dense
disparity map is first transformed to better distinguish between damaged and
undamaged road areas. To achieve greater disparity transformation efficiency,
golden section search and dynamic programming are utilized to estimate the
transformation parameters. Otsu's thresholding method is then used to extract
potential undamaged road areas from the transformed disparity map. The
disparities in the extracted areas are modeled by a quadratic surface using
least squares fitting. To improve disparity map modeling robustness, the
surface normal is also integrated into the surface modeling process.
Furthermore, random sample consensus is utilized to reduce the effects caused
by outliers. By comparing the difference between the actual and modeled
disparity maps, the potholes can be detected accurately. Finally, the point
clouds of the detected potholes are extracted from the reconstructed 3D road
surface. The experimental results show that the successful detection accuracy
of the proposed system is around 98.7% and the overall pixel-level accuracy is
approximately 99.6%.

本篇论文介绍了一种精确可靠且计算效率高的路面坑洞检测算法，该算法基于非常密集的视差图，利用双黄金分割搜索和动态规划来估计转换参数，然后通过二次曲面拟合表征提取潜在未受损路面区域，通过随机抽样共识算法减少异常点带来的干扰，最后利用重建的三维路面模型提取检测的路面坑洞点云。实验结果表明，该方法的成功检测率约为 98.7％，整体精度约为 99.6％。