Machine learning
630 paper annotations
630 paper annotations
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3D CoMPaT is a richly annotated large-scale dataset of rendered compositions of Materials on Parts of thousands of unique 3D Models. This dataset primarily focuses on stylizing 3D shapes at part-level with compatible materials. Each object with the applied part-material compositions is rendered from four equally spaced views as well as four randomized views. We introduce a new task, called Grounded CoMPaT Recognition (GCR), to collectively recognize and ground compositions of materials on parts of 3D objects. We present two variations of this task and adapt state-of-art 2D/3D deep learning methods to solve the problem as...
An open multi-sensor dataset for autonomous driving research. This dataset comprises semantically segmented images, semantic point clouds, and 3D bounding boxes. In addition, it contains unlabelled 360 degree camera images, lidar, and bus data for three sequences. The dataset supports learning tasks such as semantic segmentation, 3D object detection, SLAM, and depth estimation. We hope this dataset will further facilitate active research and development in AI, computer vision, and robotics for autonomous driving.