Baorui Ma(马宝睿)

I am a researcher at Beijing Academy of Artificial Intelligence (BAAI). I received my PhD degree from the School of Software, Tsinghua University, advised by Prof. Yu-Shen Liu.

My research interests lie in the area of 3D computer vision, 3D foundation models, 3D reconstruction, multi-view 3D reconstruction and surface reconstruction from point clouds.

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headshot
Research

(*: Equal Contribution, #: corresponding author)

UDiFF: Generating Conditional Unsigned Distance Fields with Optimal Wavelet Diffusion
Junsheng Zhou* , Weiqi Zhang*, Baorui Ma#, Kanle Shi, Yu-Shen Liu#, Zhizhong Han
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024
project page | arXiv | code

UDiFF is a 3D diffusion model for unsigned distance fields (UDFs) which is capable to generate textured 3D shapes with open surfaces from text conditions or unconditionally.

CAP-UDF: Learning Unsigned Distance Functions Progressively from Raw Point Clouds with Consistency-Aware Field Optimization
Junsheng Zhou* , Baorui Ma*, Shujuan Li, Yu-Shen Liu#, Yi Fang, Zhizhong Han
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2024
project page | IEEE Xplore | arXiv | code

We present CAP-UDF to represent shapes and scenes with arbitrary architecture by learning a Consistency-Aware unsigned distance function Progressively.

3D-OAE: Occlusion Auto-Encoders for Self-Supervised Learning on Point Clouds
Junsheng Zhou* , Xin Wen*, Baorui Ma, Yu-Shen Liu#, Yue Gao, Yi Fang, Zhizhong Han
IEEE International Conference on Robotics and Automation (ICRA), 2024 (Oral)
project page | arXiv | code

We present 3D-OAE, a novel self-supervised point cloud representation learning framework which is highly efficient and can be further transferred to various downstream tasks.

GeoDream: Disentangling 2D and Geometric Priors for High-Fidelity and Consistent 3D Generation
Baorui Ma*#, Haoge Deng*, Junsheng Zhou , Yu-Shen Liu, Tiejun Huang, Xinlong Wang#
arXiv 2023.
project page | arXiv | code

We present GeoDream, a 3D generation method that incorporates explicit generalized 3D priors with 2D diffusion priors to enhance the capability of obtaining unambiguous 3D consistent geometric structures without sacrificing diversity or fidelity.

Uni3D: Exploring Unified 3D Representation at Scale
Junsheng Zhou* , Jinsheng Wang*, Baorui Ma*#, Yu-Shen Liu, Tiejun Huang, Xinlong Wang#
International Conference on Learning Representations (ICLR, TH-CPL A), 2024 (Spotlight, ~5% acceptance rate)
Model Zoo | arXiv | code

We present Uni3D, a unified and scalable 3D pretraining framework for large-scale 3D representation learning, and explore its limits at the scale of one billion parameters.

Differentiable Registration of Images and LiDAR Point Clouds with VoxelPoint-to-Pixel Matching
Junsheng Zhou* , Baorui Ma*, Wenyuan Zhang, Yi Fang, Yu-Shen Liu#, Zhizhong Han
Conference on Neural Information Processing Systems (NeurIPS, CCF-A), 2023 (Spotlight, ~3.6% acceptance rate)
project page | arXiv | code
Learning a More Continuous Zero Level Set in Unsigned Distance Fields through Level Set Projection
Junsheng Zhou* , Baorui Ma*, Shujuan Li, Yu-Shen Liu#, Zhizhong Han
IEEE/CVF International Conference on Computer Vision (ICCV, CCF-A), 2023
project page | arXiv | code
Learning Signed Distance Functions from Noisy 3D Point Clouds via Noise to Noise Mapping
Baorui Ma, Yu-Shen Liu#, Zhizhong Han
International Conference on Machine Learning (ICML, CCF-A), 2023 (Oral, ~2.3% acceptance rate)
paper | code
Towards Better Gradient Consistency for Neural Signed Distance Functions via Level Set Alignment
Baorui Ma*, Junsheng Zhou* , Yu-Shen Liu#, Zhizhong Han
Conference on Computer Vision and Pattern Recognition (CVPR, CCF-A), 2023
paper | project page | code
NeAF: Learning Neural Angle Fields for Point Normal Estimation
Shujuan Li*, Junsheng Zhou* Baorui Ma, Yu-Shen Liu#, Zhizhong Han
AAAI Conference on Computing on Artificial Intelligence (AAAI, CCF-A), 2023 (Oral, ~10% acceptance rate)
paper | project page | code
Learning Consistency-Aware Unsigned Distance Functions Progressively from Raw Point Clouds
Junsheng Zhou* , Baorui Ma* , Yu-Shen Liu#, Yi Fang, Zhizhong Han
Conference on Neural Information Processing Systems (NeurIPS, CCF-A), 2022
paper | project page | code

We present CAP-UDF to represent shapes and scenes with arbitrary architecture by learning a Consistency-Aware unsigned distance function Progressively.

Reconstructing Surfaces for Sparse Point Clouds with On-Surface Priors
Baorui Ma, Yu-Shen Liu#, Zhizhong Han
Conference on Computer Vision and Pattern Recognition (CVPR, CCF-A), 2022
paper | project page | code
Surface Reconstruction from Point Clouds by Learning Predictive Context Priors
Baorui Ma, Yu-Shen Liu#, Matthias Zwicker Zhizhong Han
Conference on Computer Vision and Pattern Recognition (CVPR, CCF-A), 2022
paper | project page | code
Neural-Pull: Learning Signed Distance Functions from Point Clouds by Learning to Pull Space onto Surfaces
Baorui Ma*, Zhizhong Han* Yu-Shen Liu#, Matthias Zwicker
International Conference on Machine Learning (ICML, CCF-A), 2021 (Spotlight)

paper | code
Reconstructing 3D Shapes from Multiple Sketches using Direct Shape Optimization
Zhizhong Han, Baorui Ma, Matthias Zwicker, Yu-Shen Liu#
IEEE Transactions on Image Processing (SCI, Impact factor: 9.34) (TIP, CCF-A), 2020
paper | Demo
Honors and Awards
  • Outstanding Graduate of Tsinghua University, Beijing (北京市优秀毕业生), 2023.
  • Doctoral National Scholarship, Tsinghua University (博士国家奖学金) (2 graduates per year in School of Software), 2022.
Academic Services
  • Conference Reviewer: ICML, NeurIPS, CVPR, ICCV.

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Last updated: Oct 2023