SketchANIMAR: Sketch-based 3D Animal Fine-Grained Retrieval

PubDate: Apr 2023

Teams: University of Science,;National University;University of Dayton;University of Information Technology;National Institute of Informatics

Writers: Trung-Nghia Le, Tam V. Nguyen, Minh-Quan Le, Trong-Thuan Nguyen, Viet-Tham Huynh, Trong-Le Do, Khanh-Duy Le, Mai-Khiem Tran, Nhat Hoang-Xuan, Thang-Long Nguyen-Ho, Vinh-Tiep Nguyen, Nhat-Quynh Le-Pham, Huu-Phuc Pham, Trong-Vu Hoang, Quang-Binh Nguyen, Trong-Hieu Nguyen-Mau, Tuan-Luc Huynh, Thanh-Danh Le, Ngoc-Linh Nguyen-Ha, Tuong-Vy Truong-Thuy, Truong Hoai Phong, Tuong-Nghiem Diep, Khanh-Duy Ho, Xuan-Hieu Nguyen, Thien-Phuc Tran, Tuan-Anh Yang, Kim-Phat Tran, Nhu-Vinh Hoang, Minh-Quang Nguyen, Hoai-Danh Vo, Minh-Hoa Doan, Hai-Dang Nguyen, Akihiro Sugimoto, Minh-Triet Tran

PDF: SketchANIMAR: Sketch-based 3D Animal Fine-Grained Retrieval

Abstract

The retrieval of 3D objects has gained significant importance in recent years due to its broad range of applications in computer vision, computer graphics, virtual reality, and augmented reality. However, the retrieval of 3D objects presents significant challenges due to the intricate nature of 3D models, which can vary in shape, size, and texture, and have numerous polygons and vertices. To this end, we introduce a novel SHREC challenge track that focuses on retrieving relevant 3D animal models from a dataset using sketch queries and expedites accessing 3D models through available sketches. Furthermore, a new dataset named ANIMAR was constructed in this study, comprising a collection of 711 unique 3D animal models and 140 corresponding sketch queries. Our contest requires participants to retrieve 3D models based on complex and detailed sketches. We receive satisfactory results from eight teams and 204 runs. Although further improvement is necessary, the proposed task has the potential to incentivize additional research in the domain of 3D object retrieval, potentially yielding benefits for a wide range of applications. We also provide insights into potential areas of future research, such as improving techniques for feature extraction and matching, and creating more diverse datasets to evaluate retrieval performance.