Learning Super-resolved Depth from Active Gated Imaging

12/05/2019
by   Tobias Gruber, et al.
0

Environment perception for autonomous driving is doomed by the trade-off between range-accuracy and resolution: current sensors that deliver very precise depth information are usually restricted to low resolution because of technology or cost limitations. In this work, we exploit depth information from an active gated imaging system based on cost-sensitive diode and CMOS technology. Learning a mapping between pixel intensities of three gated slices and depth produces a super-resolved depth map image with respectable relative accuracy of 5 aligned with pixel intensity values.

READ FULL TEXT

page 1

page 6

research
04/19/2013

A Joint Intensity and Depth Co-Sparse Analysis Model for Depth Map Super-Resolution

High-resolution depth maps can be inferred from low-resolution depth mea...
research
05/31/2016

Semantic-Aware Depth Super-Resolution in Outdoor Scenes

While depth sensors are becoming increasingly popular, their spatial res...
research
12/04/2021

Gated2Gated: Self-Supervised Depth Estimation from Gated Images

Gated cameras hold promise as an alternative to scanning LiDAR sensors w...
research
10/07/2022

Simulating single-photon detector array sensors for depth imaging

Single-Photon Avalanche Detector (SPAD) arrays are a rapidly emerging te...
research
11/20/2020

Robust super-resolution depth imaging via a multi-feature fusion deep network

Three-dimensional imaging plays an important role in imaging application...
research
03/11/2020

Uncertainty depth estimation with gated images for 3D reconstruction

Gated imaging is an emerging sensor technology for self-driving cars tha...
research
08/07/2017

Learning for Active 3D Mapping

We propose an active 3D mapping method for depth sensors, which allow in...

Please sign up or login with your details

Forgot password? Click here to reset