Hueting, M;
Reddy, P;
Kim, V;
Yumer, E;
Carr, N;
Mitra, NJ;
(2018)
SEETHROUGH: Finding Objects in Heavily Occluded Indoor Scene Images.
In:
(Proceedings) 6th International Conference on 3D Vision (3DV).
(pp. pp. 267-276).
IEEE: Verona, Italy.
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Abstract
Discovering 3D arrangements of objects from single indoor images is important given its many applications such as interior design and content creation for virtual environments. Although heavily researched in the recent years, existing approaches break down under medium to heavy occlusion as the core image-space region detection module fails in absence of directly visible cues. Instead, we take into account holistic contextual 3D information, exploiting the fact that objects in indoor scenes co-occur mostly in typical configurations. First, we use a neural network trained on real indoor annotated images to extract 2D keypoints, and feed them to a 3D candidate object generation stage. Then, we solve a global selection problem among these candidates using pairwise co-occurrence statistics discovered from a large 3D scene database. We iterate the process allowing for candidates with low keypoint response to be incrementally detected based on the location of the already discovered nearby objects. We demonstrate significant performance improvement over combinations of state-of-the-art methods, especially for scenes with moderately to severely occluded objects.
| Type: | Proceedings paper |
|---|---|
| Title: | SEETHROUGH: Finding Objects in Heavily Occluded Indoor Scene Images |
| Event: | 6th International Conference on 3D Vision (3DV) |
| Location: | Verona, ITALY |
| Dates: | 05 September 2018 - 08 September 2018 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/3DV.2018.00039 |
| Publisher version: | https://doi.org/10.1109/3DV.2018.00039 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Science & Technology, Technology, Computer Science, Artificial Intelligence, Engineering, Electrical & Electronic, Computer Science, Engineering |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10036763 |
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