The physical and textural attributes of objects have been widely studied for recognition, detection and segmentation tasks in computer vision.~A number of datasets, such as large scale ImageNet, have been proposed for feature learning using data hungry deep neural networks and for hand-crafted feature extraction. To intelligently interact with objects, robots and intelligent machines need the ability to infer beyond the traditional physical/textural attributes, and understand/learn visual cues, called visual affordances, for affordance recognition, detection and segmentation. To date there is no publicly available large dataset for visual affordance understanding and learning. In this paper, we introduce a large scale multi-view RGBD visual affordance learning dataset, a benchmark of 47210 RGBD images from 37 object categories, annotated with 15 visual affordance categories. To the best of our knowledge, this is the first ever and the largest multi-view RGBD visual affordance learning dataset. We benchmark the proposed dataset for affordance segmentation and recognition tasks using popular Vision Transformer and Convolutional Neural Networks. Several state-of-the-art deep learning networks are evaluated each for affordance recognition and segmentation tasks. Our experimental results showcase the challenging nature of the dataset and present definite prospects for new and robust affordance learning algorithms. The dataset is publicly available at https://sites.google.com/view/afaqshah/dataset.

翻译：物体的物理属性和纹理属性已在计算机视觉的识别、检测和分割任务中得到广泛研究。例如大规模ImageNet等数据集已被提出，用于数据驱动的深度神经网络特征学习及手工特征提取。为了与物体进行智能交互，机器人和智能设备需要具备超越传统物理/纹理属性的推断能力，理解并学习被称为视觉可供性的视觉线索，以实现可供性识别、检测与分割。目前尚无公开的大规模数据集用于视觉可供性理解与学习。本文提出一个大规模多视角RGBD视觉可供性学习数据集，包含来自37个物体类别的47210张RGBD图像，并标注了15种视觉可供性类别。据我们所知，这是首个且规模最大的多视角RGBD视觉可供性学习数据集。我们利用流行的Vision Transformer和卷积神经网络对数据集进行了可供性分割与识别任务的基准测试。多个最先进的深度学习网络分别针对可供性识别和分割任务进行了评估。实验结果表明该数据集具有挑战性，并为开发新型鲁棒的可供性学习算法提供了明确前景。该数据集公开于https://sites.google.com/view/afaqshah/dataset。