Disambiguating Monocular Depth Estimation with a Single Transient

Mark Nishimura, David B. Lindell, Christopher A. Metzler, Gordon Wetzstein

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations


Monocular depth estimation algorithms successfully predict the relative depth order of objects in a scene. However, because of the fundamental scale ambiguity associated with monocular images, these algorithms fail at correctly predicting true metric depth. In this work, we demonstrate how a depth histogram of the scene, which can be readily captured using a single-pixel time-resolved detector, can be fused with the output of existing monocular depth estimation algorithms to resolve the depth ambiguity problem. We validate this novel sensor fusion technique experimentally and in extensive simulation. We show that it significantly improves the performance of several state-of-the-art monocular depth estimation algorithms.
Original languageEnglish (US)
Title of host publicationComputer Vision – ECCV 2020
PublisherSpringer International Publishing
Number of pages17
ISBN (Print)9783030585884
StatePublished - Nov 12 2020
Externally publishedYes


Dive into the research topics of 'Disambiguating Monocular Depth Estimation with a Single Transient'. Together they form a unique fingerprint.

Cite this