Nan Wang   王楠

I am a final-year Computer Science Master's student at CAD Research Center, Tongji University, and currently an Algorithm Researcher Intern at BAAI.

My research focuses on 3D Vision, including neural rendering, world model, and related technologies. I aim to leverage sophisticated 3D assets to construct immersive synthetic environments, which I envision as foundational tools for advancing AR/VR systems and robotics applications.

If you find any research interests overlap—whether in 3D vision, synthetic data, or AI-driven simulation—please don't hesitate to contact me via email. I welcome opportunities for collaboration and am excited to explore shared ideas or projects!

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• [02/2025] I have been on an internship at Beijing Academy of Artificial Intelligence.
• [02/2025] Two papers are accepted to ICRA 2025.
• [01/2025] One paper was accepted to ICASSP 2025.

Much of my research is about inferring the physical world (shape, motion, color, light, etc) from images and 3D raw data. Representative papers are highlighted.

We leverage the 3D geometry information and the semantic features to address the challenge of 3D instance segmentation.

We use semantic features derived from 2D foundation model to revolutionize the material property optimization for 3DGS.

A key sub-problem: meta-learning from learning curves is an mature but gradually attention area within the field of meta-learning.

We combine local and global features of the structures and performance to perform few-shot point cloud semantic segmentation.

We use 3D Gaussain Splating to build a novel robotics simulator, the physical simulation is implemented by ISSAC SIM while the photorealistic render is implemented by 3DGS.

I use 3D Gaussian Splatting and nerfstudio to build an implementation, which can accomplish the 3D scene decomposition and Reconstruction.