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Beijia Lu I am a senior student at the Department of Mathematics, City University of Hong Kong. And I was also an exchange student at Department of Computer Science, National University of Singapore. Currently, I am a research intern at CMU Robotics Institute, supervised by Prof.Jun-Yan Zhu. I am broadly interested in 3D vision and robot learning, especially realistic 3D content generation using physical and neural representations. . Before that I was a summer intern at CCVL@Johns Hopkins University, supervised by Prof.Alan Yuille. I was also a research intern at Show Lab @ National University of Singapore, advised by Prof.Mike Z. Shou. |
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ColonNeRF: Hierarchical Neural Radiance Fields for High-Fidelity Long-Sequence Colon Reconstruction
Yufei Shi*, Beijia Lu*, Jia-Wei Liu, Ming Li, Mike Zheng Shou Submitted to IEEE Trans. Medical Imaging arXiv / Project Page We propose a neural rendering framework for reconstructing the entire colon. |
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Adding 3D Geometry Control to Diffusion Models
Wufei Ma*, Qihao Liu*, Jiahao Wang*, Xiaoding Yuan, Angtian Wang, Yi Zhang, Zihao Xiao, Guofeng Zhang, Beijia Lu, Ruxiao Duan, Yongrui Qi, Adam Kortylewski, Yaoyao Liu, Alan Yuille ICLR (Spotlight), 2024 arXiv / Project Page We generate images of the 3D objects taken from 3D shape repositories (e.g., ShapeNet and Objaverse), render them from a variety of poses and viewing directions, compute the edge maps of the rendered images, and use these edge maps as visual prompts to generate realistic images. |
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Left-right Discrepancy for Adversarial Attack on Stereo Networks
Pengfei Wang, Xiaofei Hui, Beijia Lu, Nimrod Lilith, Jun Liu, Sameer Alam Submitted to CVPR, 2024 arXiv We introduce an adversarial attack method that generates perturbations to amplify discrepancies between left and right image features in stereo matching neural networks. |
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Dual-View Selective Instance Segmentation Network for Unstained Live Adherent Cells in Differential Interference Contrast Images
Fei Pan, Yutong Wu, Kangning Cui, Shuxun Chen, Yanfang Li, Yaofang Liu, Adnan Shakoor, Han Zhao, Beijia Lu, Shaohua Zhi, Raymond Chan, Dong Sun Submitted to Medical Image Anaylsis arXiv We develop a novel deep-learning algorithm for segmenting unstained adherent cells in DIC images, achieving significant advancements in cell instance segmentation accuracy. |
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Thank Jon Barron for sharing his website's source code. |