Related papers: Deconstructing Self-Supervised Monocular Reconstruction: The Design Decisions that Matter

Deconstructing Self-Supervised Monocular Reconstruction: The Design Decisions that Matter

URL: http://arxiv.org/abs/2208.01489v1
Date: Tue, 2 Aug 2022 14:38:53 GMT
Title: Deconstructing Self-Supervised Monocular Reconstruction: The Design Decisions that Matter
Authors: Jaime Spencer Martin, Chris Russell, Simon Hadfield, Richard Bowden
Abstract summary: This paper presents a framework to evaluate state-of-the-art contributions to self-supervised monocular depth estimation. It includes pretraining, backbone, architectural design choices and loss functions. We re-implement, validate and re-evaluate 16 state-of-the-art contributions and introduce a new dataset.
Score: 63.5550818034739
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
Abstract: This paper presents an open and comprehensive framework to systematically evaluate state-of-the-art contributions to self-supervised monocular depth estimation. This includes pretraining, backbone, architectural design choices and loss functions. Many papers in this field claim novelty in either architecture design or loss formulation. However, simply updating the backbone of historical systems results in relative improvements of 25%, allowing them to outperform the majority of existing systems. A systematic evaluation of papers in this field was not straightforward. The need to compare like-with-like in previous papers means that longstanding errors in the evaluation protocol are ubiquitous in the field. It is likely that many papers were not only optimized for particular datasets, but also for errors in the data and evaluation criteria. To aid future research in this area, we release a modular codebase, allowing for easy evaluation of alternate design decisions against corrected data and evaluation criteria. We re-implement, validate and re-evaluate 16 state-of-the-art contributions and introduce a new dataset (SYNS-Patches) containing dense outdoor depth maps in a variety of both natural and urban scenes. This allows for the computation of informative metrics in complex regions such as depth boundaries.