STADIE-Net: Stagewise Disparity Estimation from Stereo Event-based Cameras - Inria - Institut national de recherche en sciences et technologies du numérique
Poster Communications Year : 2021

STADIE-Net: Stagewise Disparity Estimation from Stereo Event-based Cameras

Abstract

Event-based cameras complement the frame based cameras in low-light conditions and high dynamic range scenarios that a robot can encounter for scene understanding and navigation. Apart from that, the comparatively cheaper cost in relation to a LiDAR sensor makes this a viable candidate when designing a sensor suite for a robot designed to operate in a dynamic environment. One of the challenges that the sensor suite needs to address is the ability to provide a 3D scene understanding of the environment that would enable the robot to localize obstacles in the environment. This work evaluates the accuracy with which an event-based camera can support this task by providing the disparity estimate between left and right camera frame which can be utilized to calculate the depth of surrounding. A new deep network architecture, named STADIE-Net is proposed that takes advantage of stagewise refinement and prediction of disparity using events from 2 neuromorphic cameras in a stereo setup. The method utilizes voxel grid representation for events as input and proposes a 4 stage network going from coarse to finer disparity prediction. The model is trained and evaluated on the publicly released DSEC dataset that has data recorded from multiple cities using event-based and frame-based cameras mounted on a moving vehicle. Experimental results show comparable accuracy with baseline method provided for DSEC dataset.
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Dates and versions

hal-03368215 , version 1 (06-10-2021)

Identifiers

  • HAL Id : hal-03368215 , version 1

Cite

Abhishek Tomy, Anshul Paigwar, Alessandro Renzaglia, Christian Laugier. STADIE-Net: Stagewise Disparity Estimation from Stereo Event-based Cameras. CVPR 2021 - Conference on Computer Vision and Pattern Recognition, Jun 2021, Nashville, United States. pp.1-4, 2021. ⟨hal-03368215⟩
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