The FineView Dataset:A 3D Scanned Multi-View Object Dataset of Fine-Grained Category Instances

Suguru Onda, Ryan Farrell; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 5623-5634

In the past decade state-of-the-art deep learning models have shown impressive performance in many computer vision tasks by learning from large and diverse image datasets. Most of these datasets consist of web-scraped image collections. This approach however makes it very challenging to obtain desirable data such as multiple views of the same object 3D geometric information or camera parameters for a large-scale image dataset. In this paper we propose a 3D-scanned multi-view 2D image dataset of fine-grained category instances with accurate camera calibration parameters. We describe our bi-directional multi-camera and 3D scanning system and the data collection pipeline. Our target objects are relatively small highly-detailed fine-grained category instances such as insects. We present this dataset as a contribution to fine-grained visual categorization 3D representation learning and for use in other computer vision tasks. The final version of the FineView dataset is available at: https://github.com/byu-vision/fineview