Robust Regression for Monocular Depth Estimation

Julian Lienen, Nils Nommensen, Ralph Ewerth, Eyke Hüllermeier
Proceedings of The 13th Asian Conference on Machine Learning, PMLR 157:1001-1016, 2021.

Abstract

Learning accurate models for monocular depth estimation requires precise depth annotation as e.g. gathered through LiDAR scanners. Because the data acquisition with sensors of this kind is costly and does not scale well in general, less advanced depth sources, such as time-of-flight cameras, are often used instead. However, these sensors provide less reliable signals, resulting in imprecise depth data for training regression models. As shown in idealized environments, the noise produced by commonly used RGB-D sensors violates standard statistical assumptions of regression methods, such as least squares estimation. In this paper, we investigate whether robust regression methods, which are more tolerant toward violations of statistical assumptions, can mitigate the effects of low-quality data. As a viable alternative to established approaches of that kind, we propose the use of so-called superset learning, where the original data is replaced by (less precise but more reliable) set-valued data. To evaluate and compare the methods, we provide an extensive empirical study on common benchmark data for monocular depth estimation. Our results clearly show the superiority of robust variants over conventional regression.

Cite this Paper

BibTeX
@InProceedings{pmlr-v157-lienen21a, title = {Robust Regression for Monocular Depth Estimation}, author = {Lienen, Julian and Nommensen, Nils and Ewerth, Ralph and H\"ullermeier, Eyke}, booktitle = {Proceedings of The 13th Asian Conference on Machine Learning}, pages = {1001--1016}, year = {2021}, editor = {Balasubramanian, Vineeth N. and Tsang, Ivor}, volume = {157}, series = {Proceedings of Machine Learning Research}, month = {17--19 Nov}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v157/lienen21a/lienen21a.pdf}, url = {https://proceedings.mlr.press/v157/lienen21a.html}, abstract = {Learning accurate models for monocular depth estimation requires precise depth annotation as e.g. gathered through LiDAR scanners. Because the data acquisition with sensors of this kind is costly and does not scale well in general, less advanced depth sources, such as time-of-flight cameras, are often used instead. However, these sensors provide less reliable signals, resulting in imprecise depth data for training regression models. As shown in idealized environments, the noise produced by commonly used RGB-D sensors violates standard statistical assumptions of regression methods, such as least squares estimation. In this paper, we investigate whether robust regression methods, which are more tolerant toward violations of statistical assumptions, can mitigate the effects of low-quality data. As a viable alternative to established approaches of that kind, we propose the use of so-called superset learning, where the original data is replaced by (less precise but more reliable) set-valued data. To evaluate and compare the methods, we provide an extensive empirical study on common benchmark data for monocular depth estimation. Our results clearly show the superiority of robust variants over conventional regression.} }
Endnote
%0 Conference Paper %T Robust Regression for Monocular Depth Estimation %A Julian Lienen %A Nils Nommensen %A Ralph Ewerth %A Eyke Hüllermeier %B Proceedings of The 13th Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2021 %E Vineeth N. Balasubramanian %E Ivor Tsang %F pmlr-v157-lienen21a %I PMLR %P 1001--1016 %U https://proceedings.mlr.press/v157/lienen21a.html %V 157 %X Learning accurate models for monocular depth estimation requires precise depth annotation as e.g. gathered through LiDAR scanners. Because the data acquisition with sensors of this kind is costly and does not scale well in general, less advanced depth sources, such as time-of-flight cameras, are often used instead. However, these sensors provide less reliable signals, resulting in imprecise depth data for training regression models. As shown in idealized environments, the noise produced by commonly used RGB-D sensors violates standard statistical assumptions of regression methods, such as least squares estimation. In this paper, we investigate whether robust regression methods, which are more tolerant toward violations of statistical assumptions, can mitigate the effects of low-quality data. As a viable alternative to established approaches of that kind, we propose the use of so-called superset learning, where the original data is replaced by (less precise but more reliable) set-valued data. To evaluate and compare the methods, we provide an extensive empirical study on common benchmark data for monocular depth estimation. Our results clearly show the superiority of robust variants over conventional regression.
APA
Lienen, J., Nommensen, N., Ewerth, R. & Hüllermeier, E.. (2021). Robust Regression for Monocular Depth Estimation. Proceedings of The 13th Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 157:1001-1016 Available from https://proceedings.mlr.press/v157/lienen21a.html.

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