12 datasets found

The dataset used in the paper for efficient IoT inference via context-awareness.

Places205 is a dataset of 2.5 million images from 205 categories, with 12,000 images per category.

3D Convolution Neural Networks (CNNs) have been widely applied to 3D scene understanding, such as video analysis and volumetric image recognition.

The UniPerf dataset is a benchmark for perceptual parsing for scene understanding.

The Places dataset is a large-scale dataset for scene recognition, containing 1 million images from 365 categories.

The Argoverse dataset is a large-scale dataset for autonomous driving, containing 3D point clouds, semantic segmentation masks, and instance segmentation masks.

The Replica dataset contains 18 various highly photo-realistic indoor environments. It provides dense-mesh, high-resolution RGBD images and a large range of instance annotations...

The Visual Genome dataset is a large-scale visual question answering dataset, containing 1.5 million images, each with 15-30 annotated entities, attributes, and relationships.

The dataset used for training and validation of the proposed approach to combine semantic segmentation and dense outlier detection.

Common Objects in 3D: Large-scale learning and evaluation of real-life 3D category reconstruction

Google Scanned Objects is a real-scanned 3D object dataset and we use all its 1030 samples for evaluation unlike existing works [29, 31] that only use 30 of them.

A large-scale dataset of 3D objects for training and testing 3D reconstruction models.