Bayesian Time-of-Flight for Realtime Shape, Illumination and Albedo
Amit Adam, Christoph Dann, Omer Yair, Shai Mazor, Sebastian Nowozin

TL;DR
This paper introduces a flexible probabilistic model for real-time depth, illumination, and albedo estimation using a novel pulsed TOF camera with general exposure profiles, achieving state-of-the-art results.
Contribution
It presents a new generative probabilistic framework for pulsed TOF cameras, enabling real-time inference and modeling multipath effects without additional computational cost.
Findings
State-of-the-art depth imaging accuracy.
Effective real-time depth, albedo, and ambient light estimation.
Seamless integration of multipath modeling.
Abstract
We propose a computational model for shape, illumination and albedo inference in a pulsed time-of-flight (TOF) camera. In contrast to TOF cameras based on phase modulation, our camera enables general exposure profiles. This results in added flexibility and requires novel computational approaches. To address this challenge we propose a generative probabilistic model that accurately relates latent imaging conditions to observed camera responses. While principled, realtime inference in the model turns out to be infeasible, and we propose to employ efficient non-parametric regression trees to approximate the model outputs. As a result we are able to provide, for each pixel, at video frame rate, estimates and uncertainty for depth, effective albedo, and ambient light intensity. These results we present are state-of-the-art in depth imaging. The flexibility of our approach allows us to…
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Videos
Bayesian Time-of-Flight for Realtime Shape, Illumination, and Albedo· youtube
Taxonomy
TopicsAdvanced Optical Sensing Technologies · Optical measurement and interference techniques · Remote Sensing and LiDAR Applications
