Overview The IITKGP_Fence dataset is designed for tasks related to fence-like occlusion detection, defocus blur, depth mapping, and object segmentation. The captured data vaies in scene composition, background defocus, and object occlusions. The dataset comprises both labeled and unlabeled data, as well as additional video and RGB-D data. The contains ground truth occlusion masks (GT) for the corresponding images. We created the ground truth occlusion labels in a semi-automatic way with user interaction. ## Key Dataset Features: - Fence Detection: Designed for detecting fences or fence-like structures that might occlude objects. - Defocus Blur: Also contains images and videos with blurred objects, likely to challenge detection and segmentation algorithms. - RGBD Data: Offers depth information alongside RGB images, which can be used for tasks like 3D reconstruction or occlusion handling. - Unlabeled and Labeled Data: Facilitates both supervised and unsupervised learning tasks. The Labeled <a href="https://huggingface.co/datasets/NeuroVizv0yaZ3R/IITKGP_Fence_dataset/tree/main/Labeled" style="color: darkorange;">folder</a> data provides ground truth occlusion masks, while the Unlabeled <a href="https://huggingface.co/datasets/NeuroVizv0yaZ3R/IITKGP_Fence_dataset/tree/main/Unlabeled" style="color: teal;">folder</a> data allows for further experimentation or self-supervised methods. ### Dataset Repository - GitHub Repository: <a href="https://github.com/Moushumi9medhi/Occlusion-Removal" style="color: magenta;">Occlusion-Removal</a> - Paper:Deep Generative Adversarial Network for Occlusion Removal from a Single Image - Authors:Sankaraganesh Jonna, Moushumi Medhi, Rajiv Ranjan Sahay ## Contact medhi.moushumi@iitkgp.ac.in