Runfeng Li

Hi, I'm a research assistant at Brown University, working with Prof. James Tompkin. I work on computer vision, with a current focus on computational imaging and physics-informed reconstruction.

I did my master's in computer science at Brown and my undergrad in mathematics and computer science at UIUC.

Email: runfeng_li [at] brown [dot] edu

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Publications

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.

Reference: Jon Barron's Template.