Robust Semi-Supervised Monocular Depth Estimation with Reprojected Distances

10/04/2019
by   Vitor Guizilini, et al.
7

Dense depth estimation from a single image is a key problem in computer vision, with exciting applications in a multitude of robotic tasks. Initially viewed as a direct regression problem, requiring annotated labels as supervision at training time, in the past few years a substantial amount of work has been done in self-supervised depth training based on strong geometric cues, both from stereo cameras and more recently from monocular video sequences. In this paper we investigate how these two approaches (supervised self-supervised) can be effectively combined, so that a depth model can learn to encode true scale from sparse supervision while achieving high fidelity local accuracy by leveraging geometric cues. To this end, we propose a novel supervised loss term that complements the widely used photometric loss, and show how it can be used to train robust semi-supervised monocular depth estimation models. Furthermore, we evaluate how much supervision is actually necessary to train accurate scale-aware monocular depth models, showing that with our proposed framework, very sparse LiDAR information, with as few as 4 beams (less than 100 valid depth values per image), is enough to achieve results competitive with the current state-of-the-art.

READ FULL TEXT

page 4

page 6

page 11

page 13

page 14

research
01/19/2020

FIS-Nets: Full-image Supervised Networks for Monocular Depth Estimation

This paper addresses the importance of full-image supervision for monocu...
research
05/06/2019

PackNet-SfM: 3D Packing for Self-Supervised Monocular Depth Estimation

Densely estimating the depth of a scene from a single image is an ill-po...
research
08/06/2019

Semi-Supervised Adversarial Monocular Depth Estimation

In this paper, we address the problem of monocular depth estimation when...
research
03/22/2021

Monocular Depth Estimation through Virtual-world Supervision and Real-world SfM Self-Supervision

Depth information is essential for on-board perception in autonomous dri...
research
06/03/2021

Single Image Depth Estimation using Wavelet Decomposition

We present a novel method for predicting accurate depths from monocular ...
research
03/23/2022

CroMo: Cross-Modal Learning for Monocular Depth Estimation

Learning-based depth estimation has witnessed recent progress in multipl...
research
02/28/2023

Monocular Depth Estimation using Diffusion Models

We formulate monocular depth estimation using denoising diffusion models...

Please sign up or login with your details

Forgot password? Click here to reset