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Monte Carlo time-gated rendering requires sampling light paths that not only connect a sensor to an emitter, but which also have a total travel time that falls within a narrow interval, a constraint that is difficult to importance sample. We show that this problem has an underlying geometric structure: in the joint space of position and accumulated travel time, the points yielding a time-valid connection to a given query point form a light-cone shell. Prior methods sample this shell indirectly. Steady-state algorithms sample the full space and reject points outside it, giving high variance under tight gates. Ellipsoidal path connections target a single cone surface by intersecting an ellipsoid with scene geometry, coupling cost to scene complexity. Our key observation is that shell membership is cheap to test, needing only accumulated travel time and a Euclidean distance. We therefore store the vertices of traced light subpaths in a 4D spatiotemporal hierarchy and recast time-gated connection as a range query, using pruning and importance sampling over the shell to select time-valid vertices without intersecting scene geometry. This decouples the cost of time gating from scene complexity. Within a bidirectional path tracing framework, our method significantly reduces variance over existing approaches on scenes with up to 2.4M triangles.
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Unlocking the Performance of Proximity Sensors by Utilizing Transient Histograms
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.
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- PAR ID:
- 10475942
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE Robotics and Automation Letters
- Volume:
- 8
- Issue:
- 10
- ISSN:
- 2377-3774
- Page Range / eLocation ID:
- 6843 to 6850
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1109/LRA.2023.3313069
