We provide methods which recover planar scene geometry by utilizing the
transient histograms captured by a class of close-range time-of-flight (ToF)
distance sensor. A transient histogram is a one dimensional temporal waveform
which encodes the arrival time of photons incident on the ToF sensor.
Typically, a sensor processes the transient histogram using a proprietary
algorithm to produce distance estimates, which are commonly used in several
robotics applications. Our methods utilize the transient histogram directly to
enable recovery of planar geometry more accurately than is possible using only
proprietary distance estimates, and consistent recovery of the albedo of the
planar surface, which is not possible with proprietary distance estimates
alone. This is accomplished via a differentiable rendering pipeline, which
simulates the transient imaging process, allowing direct optimization of scene
geometry to match observations. To validate our methods, we capture 3,800
measurements of eight planar surfaces from a wide range of viewpoints, and show
that our method outperforms the proprietary-distance-estimate baseline by an
order of magnitude in most scenarios. We demonstrate a simple robotics
application which uses our method to sense the distance to and slope of a
planar surface from a sensor mounted on the end effector of a robot arm.

利用短程飞行时间（ToF）距离传感器捕获的瞬态直方图，提供了一种恢复平面场景几何结构的方法。通过直接利用瞬态直方图，我们的方法在精确恢复平面几何结构方面要比仅使用专有距离估计更准确，并且可以实现对平面表面反射率的一致恢复。我们通过可微分渲染流水线来完成这一目标，该流水线模拟了瞬态成像过程，从而实现了根据观测结果直接优化场景几何以匹配观测。验证方法时，我们从各种视角捕获了 3800 个测量点，并表明我们的方法在大多数场景中比专有距离估计基准性能提高一个数量级。我们演示了一个简单的机器人应用，利用我们的方法从机器人臂的末端执行器上的传感器感知到平面表面的距离和坡度。

利用瞬态直方图提高近距离传感器的性能

Unlocking the Performance of Proximity Sensors by Utilizing Transient  Histograms

We propose an analysis-by-synthesis method for fast multi-view 3D
reconstruction of opaque objects with arbitrary materials and illumination.
State-of-the-art methods use both neural surface representations and neural
rendering. While flexible, neural surface representations are a significant
bottleneck in optimization runtime. Instead, we represent surfaces as triangle
meshes and build a differentiable rendering pipeline around triangle
rasterization and neural shading. The renderer is used in a gradient descent
optimization where both a triangle mesh and a neural shader are jointly
optimized to reproduce the multi-view images. We evaluate our method on a
public 3D reconstruction dataset and show that it can match the reconstruction
accuracy of traditional baselines and neural approaches while surpassing them
in optimization runtime. Additionally, we investigate the shader and find that
it learns an interpretable representation of appearance, enabling applications
such as 3D material editing.

提出了一种分析合成方法，用于快速多视角不透明物体的三维重建，通过将表面表示为三角形网格并围绕三角形光栅化和神经阴影构建可微分的渲染管道，优化三角形网格和神经着色器来重现多视图图像，并研究了着色器，发现它学习了可解释的外观表示。