ROSSINI: Reconstructing 3D structure from single images: a perceptual reconstruction approach
Lead Research Organisation:
University of Southampton
Department Name: Sch of Psychology
Abstract
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People |
ORCID iD |
| Wendy Adams (Principal Investigator) |
Publications
Anderson MD
(2021)
Category systems for real-world scenes.
in Journal of vision
Anderson MD
(2022)
The time-course of real-world scene perception: Spatial and semantic processing.
in iScience
Spencer J
(2023)
The Second Monocular Depth Estimation Challenge
Spencer J
(2023)
The Monocular Depth Estimation Challenge
Spencer J
(2023)
The Second Monocular Depth Estimation Challenge
| Description | Real world scene recognition and categorisation occurs rapidly for human perceivers, yet the mechanisms are poorly understood. Additionally, computer vision algorithms for estimating depth and segmenting a single monocular image accurately perform poorly with scene complexity / variability, primarily due to the paucity of training sets used. Here, we investigate both human and computer vision systems in a series of tasks related to estimating depth from a natural scene, and segmenting that scene accurately into category labels appropriate for a rich, diverse natural world. The ability of human observers to identify and organise visual information into categories is a popular metric of scene recognition and understanding in behavioral and computational research. However, categorical constructs and their labels can be somewhat arbitrary. We have developed a new algorithm for describing human-centred categorisation of scene information, outperforming previous state-of-the-art descriptions in the literature. We go on to investigate the time-course of spatial and semantic scene perception, finding that contrary to space-centred theories of human vision, rather than using spatial layout to infer semantic categories, humans exploit semantic information to discriminate spatial structure categories. Together, these findings challenge traditional 'bottom-up' views of scene perception. We made use of the SYNS dataset, a large repository of LiDAR and image data across a number of natural scene categories to evaluate state-of-the depth and semantic segmentation algorithms. In two monocular depth challenges, we demonstrate that, whilst current algorithms have shown vast improvements in their ability to accurately describe depth from a single monocular view, they perform less well when tasked with doing so across a variety of natural scenes, and for specific aspects of scenes such as assigning depth at object boundaries and accurately estimating metric depth. Our work has led to follow on research focussed on heuristics used by human perception may be able to inform and simplify the computational task. |
| Exploitation Route | Yes. Understanding how human observers organise complex visual information will help researchers to better describe human perception, but will also enable computational models that use this information in processing natural scenes to become more efficient and effective. |
| Sectors | Creative Economy Digital/Communication/Information Technologies (including Software) |
| Title | Dataset supporting the publication: The time-course of real-world scene perception: spatial and semantic processing |
| Description | This dataset is supporting the publication "The time-course of real-world scene perception: spatial and semantic processing". The data includes experimental data and key analyses (R and MATLAB scripts) that accompany the paper. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://eprints.soton.ac.uk/id/eprint/482269 |
| Description | Depth and scene gist |
| Organisation | York University Toronto |
| Country | Canada |
| Sector | Academic/University |
| PI Contribution | A collaborative research project, I am conducting the research using the SYNS dataset that was created as a key outcome of the EPSRC grant |
| Collaborator Contribution | Addition of expertise in stereo depth processing from Professor Laurie Wilcox |
| Impact | None yet |
| Start Year | 2016 |
| Description | BMVA workshop in London |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Industry/Business |
| Results and Impact | Around 60 people attended the workshop, whose theme was 3D reconstruction in both humans and machines. We had 2 international speakers. |
| Year(s) Of Engagement Activity | 2020 |
| URL | https://britishmachinevisionassociation.github.io/meetings/20-01-29-3D%20worlds%20from%202D%20images... |