Charles Ruizhongtai Qi
Research Scientist
Waymo LLC
Mountain View, CA
Email: rqi [at] stanford [dot] edu

[Publications]  [Education]  [Experiences]  [Misc] 
[Google Scholar]  [GitHub]  [LinkedIn]

I am currently a Research Scientist at Waymo (previously Google's self-driving car team). I received my Ph.D. from Stanford University (Stanford AI Lab and Geometric Computation Group), advised by Professor Leonidas J. Guibas. Prior to joining Stanford, I got my B.E. in Electronic Engineering from Tsinghua University.

My research focuses on deep learning, computer vision and 3D. I have developed novel deep learning architectures for 3D data (point clouds, volumetric grids and multi-view images) that have wide applications in 3D object classification, object part segmentation, semantic scene parsing, scene flow estimation and 3D reconstruction. Those deep architectures have been well adopted by both academic and industrial groups across the world. Recently, I have also invented several state-of-the-art methods for 3D object recognition, which reinforce current and future applications in augmented reality and robotics. If you are interested in my research or have any use cases that you want to share, feel free to contact me!

News


Publications

Deep Hough Voting for 3D Object Detection in Point Clouds, Oral Presentation, ICCV 2019
Charles R. Qi, Or Litany, Kaiming He, Leonidas J. Guibas

Best Paper Award Nomination (one of the seven among 1,075 accepted papers) [link]

We show a revive of generalize Hough voting in the era of deep learning for the task of 3D object detection in point clouds. Our voting-based detection network (VoteNet) is both fast and top performing.

paper / bibtex / code


KPConv: Flexible and Deformable Convolution for Point Clouds, ICCV 2019
Hugues Thomas, Charles R. Qi, Jean-Emmanuel Deschaud, Beatriz Marcotegui, Francois Goulette, Leonidas J. Guibas

Proposed a point centric way for deep learning on 3D point clouds with kernel point convolution (KPConv) where we define a convolution kernel as a set of spatially localized and deformable points.

paper / bibtex / code


Generating 3D Adversarial Point Clouds, CVPR 2019
Chong Xiang, Charles R. Qi, Bo Li

Proposed several novel algorithms to craft adversarial point clouds against 3D deep learning models with adversarial points perturbation and adversarial points generation.

paper / bibtex / code


FlowNet3D: Learning Scene Flow in 3D Point Clouds, CVPR 2019
Xingyu Liu*, Charles R. Qi*, Leonidas Guibas (*: equal contribution)

Proposed a novel deep neural network that learns scene flow from point clouds in an end-to-end fashion.

paper / bibtex / code


Exploring Hidden Dimensions in Parallelizing Convolutional Neural Networks, ICML 2018
Zhihao Jia, Sina Lin, Charles R. Qi, Alex Aiken

We studied how to parallelize training of deep convolutional networks beyond simple data or model parallelism. Proposed a layer-wise parallelism that allows each layer in a network to use an individual parallelization strategy.

paper / bibtex


Frustum PointNets for 3D Object Detection from RGB-D Data, CVPR 2018
Charles R. Qi, Wei Liu, Chenxia Wu, Hao Su, and Leonidas J. Guibas

Proposed a novel framework for 3D object detection with image region proposals (lifted to 3D frustums) and PointNets. Our method is simple, efficient and effective, ranking at first place for KITTI 3D object detection benchmark on all categories (11/27/2017).

paper / bibtex / code / website


PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space, NIPS 2017
Charles R. Qi, Li Yi, Hao Su, and Leonidas J. Guibas

Proposed a hierarchical neural network on point sets that captures local context. Compared with PointNet, PointNet++ achieves better performance and generalizability in complex scenes and is able to deal with non-uniform sampling density.

paper / bibtex / code / website / poster


PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation, Oral Presentation, CVPR 2017
Charles R. Qi*, Hao Su*, Kaichun Mo, and Leonidas J. Guibas (*: equal contribution)

Proposed novel neural networks to directly consume an unordered point cloud as input, without converting to other 3D representations such as voxel grids first. Rich theoretical and empirical analyses are provided.

paper / bibtex / code / website / presentation video


Shape Completion using 3D-Encoder-Predictor CNNs and Shape Synthesis, Spotlight Presentation, CVPR 2017
Angela Dai, Charles R. Qi, Matthias Niessner

A data-driven approach to complete partial 3D shapes through a combination of volumetric deep neural networks and 3D shape synthesis.

paper / bibtex / website (code & data available)


Volumetric and Multi-View CNNs for Object Classification on 3D Data, Spotlight Presentation, CVPR 2016
Charles R. Qi*, Hao Su*, Matthias Niessner, Angela Dai, Mengyuan Yan, and Leonidas J. Guibas (*: equal contribution)

Novel architectures for 3D CNNs that take volumetric or multi-view representations as input.

paper / bibtex / code / website / supp / presentation video


FPNN: Field Probing Neural Networks for 3D Data, NIPS 2016
Yangyan Li, Soeren Pirk, Hao Su, Charles R. Qi, and Leonidas J. Guibas

A very efficient 3D deep learning method for volumetric data processing that takes advantage of data sparsity in 3D fields.

paper / bibtex / code / website


Joint Embeddings of Shapes and Images via CNN Image Purification, SIGGRAPH Asia 2015
Yangyan Li*, Hao Su*, Charles R. Qi, Noa Fish, Daniel Cohen-Or, and Leonidas J. Guibas (*: equal contribution)

Cross-modality learning of 3D shapes and 2D images by neural networks. A joint embedding space that is sensitive to 3D geometry difference but agnostic to other nuisances is constructed.

paper / bibtex / code / website / live demo


Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views, Oral Presentation, ICCV 2015
Hao Su*, Charles R. Qi*, Yangyan Li, Leonidas J. Guibas (*equal contribution)

Pioneering work that shows large-scale synthetic data rendered from virtual world may greatly benefit deep learning to work in real world. Deliver a state-of-the-art viewpoint estimator.

paper / bibtex / code / website / presentation video


Education


Experiences


Professional service