Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 143-150 |
| Seitenumfang | 8 |
| Fachzeitschrift | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Jahrgang | 5 |
| Ausgabenummer | 2 |
| Publikationsstatus | Veröffentlicht - 17 Mai 2022 |
| Veranstaltung | 2022 24th ISPRS Congress on Imaging Today, Foreseeing Tomorrow, Commission II - Nice, Frankreich Dauer: 6 Juni 2022 → 11 Juni 2022 |
Abstract
The goal of this paper is 3D shape completion: given an incomplete instance of a known category, hallucinate a complete version of it that is geometrically plausible. We develop an adversarial framework that makes it possible to learn shape completion in a self-supervised fashion, only from incomplete examples. This is enabled by a discriminator network that rejects incomplete shapes, via a loss function that separately assesses local sub-regions of the generated example and accepts only regions with sufficiently high point count. This inductive bias against empty regions forces the generator to output complete shapes. We demonstrate the effectiveness of this approach on synthetic data from ShapeNet and ModelNet, and on a real mobile mapping dataset with nearly 9'000 incomplete cars. Moreover, we apply it to the KITTI autonomous driving dataset without retraining, to highlight its ability to generalise to different data characteristics.
ASJC Scopus Sachgebiete
- Physik und Astronomie (insg.)
- Instrumentierung
- Umweltwissenschaften (insg.)
- Umweltwissenschaften (sonstige)
- Erdkunde und Planetologie (insg.)
- Erdkunde und Planetologie (sonstige)
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
in: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Jahrgang 5, Nr. 2, 17.05.2022, S. 143-150.
Publikation: Beitrag in Fachzeitschrift › Konferenzaufsatz in Fachzeitschrift › Forschung › Peer-Review
}
TY - JOUR
T1 - Self-Supervised Adversarial Shape Completion
AU - Peters, Torben
AU - Schindler, Konrad
AU - Brenner, Claus
PY - 2022/5/17
Y1 - 2022/5/17
N2 - The goal of this paper is 3D shape completion: given an incomplete instance of a known category, hallucinate a complete version of it that is geometrically plausible. We develop an adversarial framework that makes it possible to learn shape completion in a self-supervised fashion, only from incomplete examples. This is enabled by a discriminator network that rejects incomplete shapes, via a loss function that separately assesses local sub-regions of the generated example and accepts only regions with sufficiently high point count. This inductive bias against empty regions forces the generator to output complete shapes. We demonstrate the effectiveness of this approach on synthetic data from ShapeNet and ModelNet, and on a real mobile mapping dataset with nearly 9'000 incomplete cars. Moreover, we apply it to the KITTI autonomous driving dataset without retraining, to highlight its ability to generalise to different data characteristics.
AB - The goal of this paper is 3D shape completion: given an incomplete instance of a known category, hallucinate a complete version of it that is geometrically plausible. We develop an adversarial framework that makes it possible to learn shape completion in a self-supervised fashion, only from incomplete examples. This is enabled by a discriminator network that rejects incomplete shapes, via a loss function that separately assesses local sub-regions of the generated example and accepts only regions with sufficiently high point count. This inductive bias against empty regions forces the generator to output complete shapes. We demonstrate the effectiveness of this approach on synthetic data from ShapeNet and ModelNet, and on a real mobile mapping dataset with nearly 9'000 incomplete cars. Moreover, we apply it to the KITTI autonomous driving dataset without retraining, to highlight its ability to generalise to different data characteristics.
KW - 3D Point Clouds
KW - Adversarial Learning
KW - Shape Completion
KW - Unsupervised Learning
UR - http://www.scopus.com/inward/record.url?scp=85132294058&partnerID=8YFLogxK
U2 - 10.5194/isprs-annals-V-2-2022-143-2022
DO - 10.5194/isprs-annals-V-2-2022-143-2022
M3 - Conference article
AN - SCOPUS:85132294058
VL - 5
SP - 143
EP - 150
JO - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
JF - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
SN - 2194-9042
IS - 2
T2 - 2022 24th ISPRS Congress on Imaging Today, Foreseeing Tomorrow, Commission II
Y2 - 6 June 2022 through 11 June 2022
ER -