Tabib, Ramesh Ashok
1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results
Kiefer, Benjamin, Kristan, Matej, Perš, Janez, Žust, Lojze, Poiesi, Fabio, Andrade, Fabio Augusto de Alcantara, Bernardino, Alexandre, Dawkins, Matthew, Raitoharju, Jenni, Quan, Yitong, Atmaca, Adem, Höfer, Timon, Zhang, Qiming, Xu, Yufei, Zhang, Jing, Tao, Dacheng, Sommer, Lars, Spraul, Raphael, Zhao, Hangyue, Zhang, Hongpu, Zhao, Yanyun, Augustin, Jan Lukas, Jeon, Eui-ik, Lee, Impyeong, Zedda, Luca, Loddo, Andrea, Di Ruberto, Cecilia, Verma, Sagar, Gupta, Siddharth, Muralidhara, Shishir, Hegde, Niharika, Xing, Daitao, Evangeliou, Nikolaos, Tzes, Anthony, Bartl, Vojtěch, Špaňhel, Jakub, Herout, Adam, Bhowmik, Neelanjan, Breckon, Toby P., Kundargi, Shivanand, Anvekar, Tejas, Desai, Chaitra, Tabib, Ramesh Ashok, Mudengudi, Uma, Vats, Arpita, Song, Yang, Liu, Delong, Li, Yonglin, Li, Shuman, Tan, Chenhao, Lan, Long, Somers, Vladimir, De Vleeschouwer, Christophe, Alahi, Alexandre, Huang, Hsiang-Wei, Yang, Cheng-Yen, Hwang, Jenq-Neng, Kim, Pyong-Kun, Kim, Kwangju, Lee, Kyoungoh, Jiang, Shuai, Li, Haiwen, Ziqiang, Zheng, Vu, Tuan-Anh, Nguyen-Truong, Hai, Yeung, Sai-Kit, Jia, Zhuang, Yang, Sophia, Hsu, Chih-Chung, Hou, Xiu-Yu, Jhang, Yu-An, Yang, Simon, Yang, Mau-Tsuen
The 1$^{\text{st}}$ Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and organized several subchallenges in this domain: (i) UAV-based Maritime Object Detection, (ii) UAV-based Maritime Object Tracking, (iii) USV-based Maritime Obstacle Segmentation and (iv) USV-based Maritime Obstacle Detection. The subchallenges were based on the SeaDronesSee and MODS benchmarks. This report summarizes the main findings of the individual subchallenges and introduces a new benchmark, called SeaDronesSee Object Detection v2, which extends the previous benchmark by including more classes and footage. We provide statistical and qualitative analyses, and assess trends in the best-performing methodologies of over 130 submissions. The methods are summarized in the appendix. The datasets, evaluation code and the leaderboard are publicly available at https://seadronessee.cs.uni-tuebingen.de/macvi.
ABD-Net: Attention Based Decomposition Network for 3D Point Cloud Decomposition
Katageri, Siddharth, Kudari, Shashidhar V, Gunari, Akshaykumar, Tabib, Ramesh Ashok, Mudenagudi, Uma
In this paper, we propose Attention Based Decomposition Network (ABD-Net), for point cloud decomposition into basic geometric shapes namely, plane, sphere, cone and cylinder. We show improved performance of 3D object classification using attention features based on primitive shapes in point clouds. Point clouds, being the simple and compact representation of 3D objects have gained increasing popularity. They demand robust methods for feature extraction due to unorderness in point sets. In ABD-Net the proposed Local Proximity Encapsulator captures the local geometric variations along with spatial encoding around each point from the input point sets. The encapsulated local features are further passed to proposed Attention Feature Encoder to learn basic shapes in point cloud. Attention Feature Encoder models geometric relationship between the neighborhoods of all the points resulting in capturing global point cloud information. We demonstrate the results of our proposed ABD-Net on ANSI mechanical component and ModelNet40 datasets. We also demonstrate the effectiveness of ABD-Net over the acquired attention features by improving the performance of 3D object classification on ModelNet40 benchmark dataset and compare them with state-of-the-art techniques.