- point cloud
- SHREC13STB: 1258 models of 90 classes
- Princeton Shape Benchmark (2003): 1,814 models collected from the web in .OFF format. Used to evaluating shape-based retrieval and analysis algorithms.
- hierachical label supports clssification of multiple granularities
- 161 classes each contain at least 4 models at most 100 models
- Dataset for IKEA 3D models and aligned images (2013): 759 images and 219 models including Sketchup (skp) and Wavefront (obj) files, good for pose estimation.
- ModelNet (2015): 127915 3D CAD models from 662 categories ModelNet10: 4899 models from 10 categories ModelNet40: 12311 models from 40 categories, all are uniformly orientated
- Shapenet 2015: 3Million+ models and 4K+ categories. A dataset that is large in scale, well organized and richly annotated.
ObjectNet3D: A Large Scale Database for 3D Object Recognition (2016) :100 categories, 90,127 images, 201,888 objects in these images and 44,147 3D shapes. Tasks: region proposal generation, 2D object detection, joint 2D detection and 3D object pose estimation, and image-based 3D shape retrieval Benchmark:
Kinect300: Abstract and noisy
- a 3D Sketch Dataset, with 300 3D Sketches. 30 classes, each with 10 sketches by utilizing a Kinect-based virtual 3D drawing system
- format: .off
Example 3D sketches (one example per class, shown in one view) of our Kinect300 dataset. Example 3D models in the targe 3D model dataset of the SHREC13STB benchmark.