I am a Ph.D. candidate in the Department of Computer Science at the National University of Singapore, under Prof. Wei Tsang Ooi. I am also closely with Prof. Benoit Cottereau from CNRS, Dr. Lai Xing Ng from A*STAR, and Prof. Ziwei Liu from Nanyang Technological University, Singapore.
My research pursues to build 3D perception and generation models that are robust, scalable, and generalizable across domains and scenarios, while requiring minimum human annotations.
I have been fortunate to collaborate with NVIDIA Research, OpenMMLab, MMLab@NTU, and ByteDance AI Lab.
🦁 I am open to discussions and collaborations in 3D scene perception, generation, and understanding. Feel free to drop me an email if you find our research backgrounds a potential match.
* equal contributions ‡ corresponding author
DynamicCity: Large-Scale LiDAR Generation from Dynamic Scenes
arXiv, 2024
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Calib3D: Calibrating Model Preferences for Reliable 3D Scene Understanding |
Is Your LiDAR Placement Optimized for 3D Scene Understanding? |
Is Your HD Map Constructor Reliable under Sensor Corruptions? |
4D Contrastive Superflows Are Dense 3D Representation Learners |
Learning to Adapt SAM for Segmenting Cross-Domain Point Clouds |
OpenESS: Event-Based Semantic Scene Understanding with Open Vocabularies |
Multi-Space Alignments Towards Universal LiDAR Segmentation |
Unified 3D and 4D Panoptic Segmentation via Dynamic Shifting Networks |
Benchmarking and Improving Bird's Eye View Perception Robustness in Autonomous Driving |
RoboDepth: Robust Out-of-Distribution Depth Estimation under Corruptions |
Segment Any Point Cloud Sequences by Distilling Vision Foundation Models |
Unsupervised Video Domain Adaptation for Action Recognition: A Disentanglement Perspective |
Towards Label-Free Scene Understanding by Vision Foundation Models |
Robo3D: Towards Robust and Reliable 3D Perception against Corruptions |
Rethinking Range View Representation for LiDAR Segmentation |
UniSeg: A Unified Multi-Modal LiDAR Segmentation Network and the OpenPCSeg Codebase |
LaserMix for Semi-Supervised LiDAR Semantic Segmentation |
CLIP2Scene: Towards Label-Efficient 3D Scene Understanding by CLIP |
ConDA: Unsupervised Domain Adaptation for LiDAR Segmentation via Regularized Domain Concatenation |
Benchmarking 3D Robustness to Common Corruptions and Sensor Failure |
The RoboDrive Challenge: Drive Anytime Anywhere in Any Condition
Technical Report, 2024
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The RoboDepth Challenge: Methods and Advancements Towards Robust Depth Estimation
Technical Report, 2023
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* equal contributions ‡ corresponding author
Multi-Modal Data-Efficient 3D Scene Understanding for Autonomous Driving
arXiv, 2024
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LargeAD: Large-Scale Cross-Sensor Data Pretraining for Autonomous Driving
arXiv, 2024
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Language-Driven Scene Understanding from Event Cameras
arXiv, 2024
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Free Lunch for Co-Saliency Detection: Context Adjustment
arXiv, 2021
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NVIDIA Research |
OpenMMLab |
Motional |
ByteDance AI Lab |