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 pro- prietary 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.

@Article{SWGG23,
  author       = "Sifferman, Carter and Wang, Yeping and Gupta, Mohit and Gleicher, Michael",
  title        = "Unlocking the Performance of Proximity Sensors by Utilizing Transient Histograms",
  journal      = "IEEE Robotics and Automation Letters",
  number       = "10",
  volume       = "8",
  pages        = "6843 - 6850",
  month        = "Oct",
  year         = "2023",
  ee           = "https://ieeexplore.ieee.org/abstract/document/10243076",
  doi          = "https://doi.org/10.1109/LRA.2023.3313069",
  projecturl   = "https://cpsiff.github.io/unlocking_proximity_sensors/index.html",
  url          = "http://graphics.cs.wisc.edu/Papers/2023/SWGG23"
}