SphereCraft: A Dataset for Spherical Keypoint Detection, Matching and Camera Pose Estimation

Christiano Gava, Yunmin Cho, Federico Raue, Sebastian Palacio, Alain Pagani, Andreas Dengel; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024, pp. 4408-4417

Abstract

This paper introduces SphereCraft, a dataset specifically designed for spherical keypoint detection, matching, and camera pose estimation. The dataset addresses the limitations of existing datasets by providing extracted keypoints from various detectors, along with their ground truth correspondences. Synthetic scenes with photo-realistic rendering and accurate 3D meshes are included, as well as real-world scenes acquired from different spherical cameras. SphereCraft enables the development and evaluation of algorithms targeting multiple camera viewpoints, advancing the state-of-the-art in computer vision tasks involving spherical images. Our dataset is available at https://dfki.github.io/spherecraftweb/.

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[bibtex]
@InProceedings{Gava_2024_WACV, author = {Gava, Christiano and Cho, Yunmin and Raue, Federico and Palacio, Sebastian and Pagani, Alain and Dengel, Andreas}, title = {SphereCraft: A Dataset for Spherical Keypoint Detection, Matching and Camera Pose Estimation}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, month = {January}, year = {2024}, pages = {4408-4417} }