Freeman Cheng (成)

Greetings! I am a graduating Computer Science undergraduate student at U of T. My research interests lie in 3D reconstruction, understanding, and generation. In my free time, I enjoy reading fiction and playing video games.

I am currently looking for a Ph.D. position starting in Fall 2025.

Curriculum Vitae

Education
  • University of Toronto
    University of Toronto
    BSc in Computer Science
    Sep. 2020 - Apr. 2025
Experience
  • Visual Computing Group @ Harvard
    Visual Computing Group @ Harvard
    Visiting Research Intern
    Oct. 2024 - Dec. 2024
  • iQua Group @ U of T
    iQua Group @ U of T
    NSERC USRA Researcher
    May. 2024 - Aug. 2024
  • Noah's Ark Lab @ Huawei Canada
    Noah's Ark Lab @ Huawei Canada
    Research Intern
    May. 2023 - Apr. 2024
  • Middleware Systems Research Group @ U of T
    Middleware Systems Research Group @ U of T
    NSERC USRA Researcher
    May. 2022 - Dec. 2022
  • BIMStudio Inc.
    BIMStudio Inc.
    Software Engineer
    Oct. 2020 - Aug. 2021
Highlight
MirrorGaussian: Reflecting 3D Gaussians for Reconstructing Mirror Reflections

Jiayue Liu*, Xiao Tang*, Freeman Cheng, Roy Yang, Zhihao Li, Jianzhuang Liu, Yi Huang, Jiaqi Lin, Shiyong Liu, Xiaofei Wu, Songcen Xu, Chun Yuan (* equal contribution)

ECCV 2024

MirrorGaussian: Reflecting 3D Gaussians for Reconstructing Mirror Reflections

Jiayue Liu*, Xiao Tang*, Freeman Cheng, Roy Yang, Zhihao Li, Jianzhuang Liu, Yi Huang, Jiaqi Lin, Shiyong Liu, Xiaofei Wu, Songcen Xu, Chun Yuan (* equal contribution)

ECCV 2024

Selected Publications (view all )
Coming soon

Ningxin Su, Freeman Cheng, Baochun Li

Submitted to IEEE INFOCOM 2024

Coming soon.

Coming soon

Ningxin Su, Freeman Cheng, Baochun Li

Submitted to IEEE INFOCOM 2024

Coming soon.

MirrorGaussian: Reflecting 3D Gaussians for Reconstructing Mirror Reflections
MirrorGaussian: Reflecting 3D Gaussians for Reconstructing Mirror Reflections

Jiayue Liu*, Xiao Tang*, Freeman Cheng, Roy Yang, Zhihao Li, Jianzhuang Liu, Yi Huang, Jiaqi Lin, Shiyong Liu, Xiaofei Wu, Songcen Xu, Chun Yuan (* equal contribution)

ECCV 2024

3D Gaussian Splatting showcases notable advancements in photo-realistic and real-time novel view synthesis. However, it faces challenges in modeling mirror reflections, which exhibit substantial appearance variations from different viewpoints. To tackle this problem, we present MirrorGaussian, the first method for mirror scene reconstruction with real-time rendering based on 3D Gaussian Splatting. The key insight is grounded on the mirror symmetry between the real-world space and the virtual mirror space. We introduce an intuitive dual-rendering strategy that enables differentiable rasterization of both the real-world 3D Gaussians and the mirrored counterpart obtained by reflecting the former about the mirror plane. All 3D Gaussians are jointly optimized with the mirror plane in an end-to-end framework. MirrorGaussian achieves high-quality and real-time rendering in scenes with mirrors, empowering scene editing like adding new mirrors and objects. Comprehensive experiments on multiple datasets demonstrate that our approach significantly outperforms existing methods, achieving state-of-the-art results.

MirrorGaussian: Reflecting 3D Gaussians for Reconstructing Mirror Reflections
MirrorGaussian: Reflecting 3D Gaussians for Reconstructing Mirror Reflections

Jiayue Liu*, Xiao Tang*, Freeman Cheng, Roy Yang, Zhihao Li, Jianzhuang Liu, Yi Huang, Jiaqi Lin, Shiyong Liu, Xiaofei Wu, Songcen Xu, Chun Yuan (* equal contribution)

ECCV 2024

3D Gaussian Splatting showcases notable advancements in photo-realistic and real-time novel view synthesis. However, it faces challenges in modeling mirror reflections, which exhibit substantial appearance variations from different viewpoints. To tackle this problem, we present MirrorGaussian, the first method for mirror scene reconstruction with real-time rendering based on 3D Gaussian Splatting. The key insight is grounded on the mirror symmetry between the real-world space and the virtual mirror space. We introduce an intuitive dual-rendering strategy that enables differentiable rasterization of both the real-world 3D Gaussians and the mirrored counterpart obtained by reflecting the former about the mirror plane. All 3D Gaussians are jointly optimized with the mirror plane in an end-to-end framework. MirrorGaussian achieves high-quality and real-time rendering in scenes with mirrors, empowering scene editing like adding new mirrors and objects. Comprehensive experiments on multiple datasets demonstrate that our approach significantly outperforms existing methods, achieving state-of-the-art results.

Applications of noisy quantum computing and quantum error mitigation to “adamantaneland”: a benchmarking study for quantum chemistry
Applications of noisy quantum computing and quantum error mitigation to “adamantaneland”: a benchmarking study for quantum chemistry

Viki Kumar Prasad, Freeman Cheng, Ulrich Fekl, Hans-Arno Jacobsen

PCCP 2023

The field of quantum computing has the potential to transform quantum chemistry. The variational quantum eigensolver (VQE) algorithm has allowed quantum computing to be applied to chemical problems in the noisy intermediate-scale quantum (NISQ) era. Applications of VQE have generally focused on predicting absolute energies instead of chemical properties that are relative energy differences and that are most interesting to chemists studying a chemical problem. We address this shortcoming by constructing a molecular benchmark data set in this work containing isomers of C10H16 and carbocationic rearrangements of C10H15+, calculated at a high-level of theory. Using the data set, we compared noiseless VQE simulations to conventionally performed density functional and wavefunction theory-based methods to understand the quality of results. We also investigated the effectiveness of a quantum state tomography-based error mitigation technique in applications of VQE under noise (simulated and real). Our findings reveal that the use of quantum error mitigation is crucial in the NISQ era and advantageous to yield almost noiseless quality results.

Applications of noisy quantum computing and quantum error mitigation to “adamantaneland”: a benchmarking study for quantum chemistry
Applications of noisy quantum computing and quantum error mitigation to “adamantaneland”: a benchmarking study for quantum chemistry

Viki Kumar Prasad, Freeman Cheng, Ulrich Fekl, Hans-Arno Jacobsen

PCCP 2023

The field of quantum computing has the potential to transform quantum chemistry. The variational quantum eigensolver (VQE) algorithm has allowed quantum computing to be applied to chemical problems in the noisy intermediate-scale quantum (NISQ) era. Applications of VQE have generally focused on predicting absolute energies instead of chemical properties that are relative energy differences and that are most interesting to chemists studying a chemical problem. We address this shortcoming by constructing a molecular benchmark data set in this work containing isomers of C10H16 and carbocationic rearrangements of C10H15+, calculated at a high-level of theory. Using the data set, we compared noiseless VQE simulations to conventionally performed density functional and wavefunction theory-based methods to understand the quality of results. We also investigated the effectiveness of a quantum state tomography-based error mitigation technique in applications of VQE under noise (simulated and real). Our findings reveal that the use of quantum error mitigation is crucial in the NISQ era and advantageous to yield almost noiseless quality results.

All publications