Haoze Zheng is a PhD student with Academy of Interdisciplinary Studies, Hong Kong University of Science and Technology (HKUST). Before that, he was an intern student at the SAIR Lab, Computer Science and Engineering (CSE), University at Buffalo (UB). His research interests that include robot perception, 3D computer vision and learning-based visual odometry.

Experience

Research Intern
- University at Buffalo (UB), USA
- Nov. 2023 - Aug. 2024

Education

PhD student
- Hong Kong University of Science and Technology (HKUST)
- Jan. 2025 - Present
Bachelor of Engineering in Internet of Things
- Beijing Jiaotong University, Beijing, China
- Sep. 2019 - July 2023

Other Information

Work Email: zhz19231211 AT gmail.com
School Email: haozezhe AT buffalo.edu

Publications

SuperPC: A Single Diffusion Model for Point Cloud Completion, Upsampling, Denoising, and Colorization.
Yi Du, Zhipeng Zhao, Shaoshu Su, Sharath Golluri, Haoze Zheng, Runmao Yao, Chen Wang.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 16953–16964, 2025.

Unifying point cloud processing via a single diffusion model
```
@inproceedings{du2025superpc,
  title = {{SuperPC}: A Single Diffusion Model for Point Cloud Completion, Upsampling, Denoising, and Colorization},
  author = {Du, Yi and Zhao, Zhipeng and Su, Shaoshu and Golluri, Sharath and Zheng, Haoze and Yao, Runmao and Wang, Chen},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  url = {https://arxiv.org/abs/2503.14558},
  year = {2025},
  pages = {16953--16964},
  code = {https://github.com/sair-lab/superpc},
  website = {https://sairlab.org/superpc/},
  cover = {/img/posts/2025-02-28-superpc/superpc_cover.mp4},
  addendum = {Unifying point cloud processing via a single diffusion model}
}
```
```
Du, Yi and Zhao, Zhipeng and Su, Shaoshu and Golluri, Sharath and Zheng, Haoze and Yao, Runmao and Wang, Chen, "SuperPC: A Single Diffusion Model for Point Cloud Completion, Upsampling, Denoising, and Colorization," IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025.
```
AirRoom: Objects Matter in Room Reidentification.
Runmao Yao, Yi Du, Zhuoqun Chen, Haoze Zheng, Chen Wang.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1385–1394, 2025.
```
@inproceedings{yao2025airroom,
  title = {{AirRoom}: Objects Matter in Room Reidentification},
  author = {Yao, Runmao and Du, Yi and Chen, Zhuoqun and Zheng, Haoze and Wang, Chen},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  url = {https://arxiv.org/abs/2503.01130},
  website = {https://sairlab.org/airroom/},
  year = {2025},
  pages = {1385--1394},
  code = {https://github.com/21yrm/AirRoom},
  cover = {/img/posts/2025-03-02-airroom/airroom.jpg}
}
```
```
Yao, Runmao and Du, Yi and Chen, Zhuoqun and Zheng, Haoze and Wang, Chen, "AirRoom: Objects Matter in Room Reidentification," IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025.
```
iKap: Kinematics-aware Planning with Imperative Learning.
Qihang Li, Zhuoqun Chen, Haoze Zheng, Haonan He, Shaoshu Su, Junyi Geng, Chen Wang.
IEEE International Conference on Robotics and Automation (ICRA), pp. 10164–10170, 2025.

First imperative learning planner that respects robot kinematics constraints
```
@inproceedings{li2025ikap,
  title = {{iKap}: Kinematics-aware Planning with Imperative Learning},
  author = {Li, Qihang and Chen, Zhuoqun and Zheng, Haoze and He, Haonan and Su, Shaoshu and Geng, Junyi and Wang, Chen},
  booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
  year = {2025},
  pages = {10164--10170},
  url = {https://arxiv.org/abs/2412.09496},
  video = {https://youtu.be/7HPAMFbHc4U},
  website = {https://sairlab.org/iKap},
  cover = {/img/posts/2024-12-12-ikap/cover.gif},
  addendum = {First imperative learning planner that respects robot kinematics constraints}
}
```
```
Li, Qihang and Chen, Zhuoqun and Zheng, Haoze and He, Haonan and Su, Shaoshu and Geng, Junyi and Wang, Chen, "iKap: Kinematics-aware Planning with Imperative Learning," IEEE International Conference on Robotics and Automation (ICRA), 2025.
```