Summary of Opensun3d: 1st Workshop Challenge on Open-vocabulary 3d Scene Understanding, by Francis Engelmann et al.
OpenSUN3D: 1st Workshop Challenge on Open-Vocabulary 3D Scene Understanding
by Francis Engelmann, Ayca Takmaz, Jonas Schult, Elisabetta Fedele, Johanna Wald, Songyou Peng, Xi Wang, Or Litany, Siyu Tang, Federico Tombari, Marc Pollefeys, Leonidas Guibas, Hongbo Tian, Chunjie Wang, Xiaosheng Yan, Bingwen Wang, Xuanyang Zhang, Xiao Liu, Phuc Nguyen, Khoi Nguyen, Anh Tran, Cuong Pham, Zhening Huang, Xiaoyang Wu, Xi Chen, Hengshuang Zhao, Lei Zhu, Joan Lasenby
First submitted to arxiv on: 23 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper provides an overview of the Open-Vocabulary 3D Scene Understanding challenge hosted at the ICCV 2023 workshop. The goal is to explore and discuss open-vocabulary tasks, including segmentation, detection, and mapping. The report presents the challenge dataset, evaluation methodology, and brief descriptions of winning methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about a big challenge in computer vision where we try to understand 3D scenes better. It’s like trying to figure out what’s going on in a room just by looking at pictures of it. The challenge is to find objects and map the space, kind of like creating a blueprint of a building. This report shows us how people worked together to solve this problem and what they came up with. |
Keywords
* Artificial intelligence * Scene understanding
