Benran Hu

I'm an MSCS student at CMU graduating in December, 2024. I'm actively looking for PhD and full-time industry opportunities.

At CMU, I work with Prof. Ioannis Gkioulekas on uncertainty quantification in differentiable and inverse rendering. Previously, I interned at Snap Research with Ivan Skorokhodov on better video generation models. I received my B.Sc. in Computer Science from HKUST, where I was advised by Prof. Chi-Keung Tang, Prof. Yu-Wing Tai, and Prof. Pedro V. Sander.

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Research

I'm interested in 3D vision, inverse rendering and 3D reconstruction, scene understanding, generative models, as well as other fields in computer vision and graphics. My past research is mainly on empowering radiance fields and other neural scene representations with the capability of scene understanding. I also work on uncertainty quantification in differentiable and inverse rendering, and accelerating real-time rendering with temporal reprojection.

Fusing multi-view SAM segmentation masks as an object field improves the performance of zero-shot 3D segmentation in NeRF.

3D instance segmentation in NeRF by matching multi-view instance masks with sparse 3D masks produced by a 3D Mask R-CNN.

We introduce 3D object detection to NeRF by sampling feature grids from radiance fields and applying a 3D detector on them.

This is another website using Jon Barron's template.