Are you sure you want to delete this access key?
Legend |
---|
DVC Managed File |
Git Managed File |
Metric |
Stage File |
External File |
Legend |
---|
DVC Managed File |
Git Managed File |
Metric |
Stage File |
External File |
This work revisits the problem of point cloud classification but on real world scans as opposed to synthetic models such as ModelNet40 that were studied in other recent works. We introduce ScanObjectNN, a new benchmark dataset containing ~15,000 object that are categorized into 15 categories with 2902 unique object instances. The raw objects are represented by a list of points with global and local coordinates, normals, colors attributes and semantic labels. We also provide part annotations, which to the best of our knowledge is the first on real-world data. From our comprehensive benchmark, we show that our dataset poses great challenges to existing point cloud classification techniques as objects from real-world scans are often cluttered with background and/or are partial due to occlusions.
We provide different variants of our scan dataset namely: OBJ_BG, PB_T25, PB_T25_R, PB_T50_R and PB_T50_RS as described in our paper. We released both the processed .h5 files and the raw .bin objects as described below.
We release all the raw object files of our ScanObjectNN dataset including all its variants.
scene_folder object_id object_class object_instance_label
x y z nx ny nz r g b instance_label semantic_label
Parts:
@inproceedings{uy-scanobjectnn-iccv19,
title = {Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data},
author = {Mikaela Angelina Uy and Quang-Hieu Pham and Binh-Son Hua and Duc Thanh Nguyen and Sai-Kit Yeung},
booktitle = {International Conference on Computer Vision (ICCV)},
year = {2019}
}
We would like to sincerely thank Tan Sang Ha, Fan Wai Shan, Xu Ting Ting, Loh Pei Huan, Luong Van An, Ng Shi Xian Bryden, Li Jingxin and Chiz Huang for helping in the part annotations.
This research project is partially supported by an internal grant from HKUST (R9429).
Press p or to see the previous file or, n or to see the next file
Are you sure you want to delete this access key?
Are you sure you want to delete this access key?
Are you sure you want to delete this access key?
Are you sure you want to delete this access key?