Runfeng Li

Hi, I am a Research Assistant at the Brown Visual Computing Group, advised by Prof. James Tompkin. Previously, I earned my M.S. in Computer Science from Brown University and B.S. in Mathematics and Computer Science from UIUC.

Email: runfeng_li [at] brown [dot] edu

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Research

I am primarily interested in computer vision, with a current focus on computational imaging, physics-based reconstruction, and related questions in scene representation.

I have worked on time-of-flight radiance fields, Gaussian splatting, and dynamic scene modeling.

We show that radiance field reconstruction from single-frequency continuous-wave raw time-of-flight images is fundamentally ill-posed. However, we find optimization biases in the 3D Gaussian parameterized radiance fields that help recover the most plausible geometry, which our model applies to dynamic scenes like fast-swinging baseball bats with quality comparable to or better than prior neural-field-based methods while retaining the efficiency of Gaussians.

We benchmark dynamic Gaussian splatting methods for monocular view synthesis, combining existing datasets and a new synthetic dataset to provide standardized comparisons and identify key factors affecting efficiency and quality.

Other Research Interests

We estimate Young's modulus by backpropagating video-reconstruction gradients through our Taichi implementation of PhysGaussian.

Reference: Jon Barron's Template.