Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data

Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data

Mikaela Angelina Uy, Quang-Hieu Pham, Binh-Son Hua, Duc Thanh Nguyen and Sai-Kit Yeung

ICCV 2019 Oral Presentation

Introduction

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. Our project page can be found here, and the arXiv version of our paper can be found here.

@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}
  }

ScanObjectNN Dataset

Documentation on dataset coming soon!

Code

Documentation on code structure coming soon!

Pre-trained Models

Pre-trained models will be released soon!

References

Our released code heavily based on each methods original repositories as cited below:

• PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation by Qi et al. (CVPR 2017 Oral Presentation).
• PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space by Qi et al. (NIPS 2017).
• Dynamic Graph CNN for Learning on Point Clouds by Wang et al. (TOG 2019).
• PointCNN: Convolution On X-Transformed Points by Li et al. (NIPS 2018).
• SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters by Xu et al. (ECCV 2018).
• 3DmFV : Three-Dimensional Point Cloud Classification in Real-Time using Convolutional Neural Networks by Ben-Shabat et al. (RA-L 2018).