Lingdong Kong
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 have research attachments and internships at NVIDIA Research, OpenMMLab, MMLab@NTU, Motional, 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.
News
- [07/2024] - Two papers (SuperFlow and SAM-UDA) accepted to ECCV 2024.
- [06/2024] - Our team won the first place in the 2024 Waymo Open Dataset Challenge.
- [04/2024] - OpenESS was selected as a highlight at CVPR 2024.
- [02/2024] - Two papers (OpenESS and M3Net) accepted to CVPR 2024.
- [12/2023] - Invited talk (slides) at Hyundai Motor Group.
- [11/2023] - We are hosting The RoboDrive Challenge at ICRA 2024.
- [09/2023] - Seal was selected as a spotlight at NeurIPS 2023.
- [09/2023] - Four papers (RoboDepth, TranSVAE, Seal, and CNS) accepted to NeurIPS 2023.
- [07/2023] - Three papers (Robo3D, RangeFormer, and UniSeg) accepted to ICCV 2023.
- [03/2023] - LaserMix was selected as a highlight at CVPR 2023.
Recent Publications [Complete List]
* equal contributions ‡ corresponding author
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4D Contrastive Superflows Are Dense 3D Representation Learners |
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Learning to Adapt SAM for Segmenting Cross-Domain Point Clouds |
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OpenESS: Event-Based Semantic Scene Understanding with Open Vocabularies |
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Multi-Space Alignments Towards Universal LiDAR Segmentation |
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Unified 3D and 4D Panoptic Segmentation via Dynamic Shifting Networks |
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Benchmarking and Improving Bird's Eye View Perception Robustness in Autonomous Driving |
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RoboDepth: Robust Out-of-Distribution Depth Estimation under Corruptions |
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Segment Any Point Cloud Sequences by Distilling Vision Foundation Models |
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Unsupervised Video Domain Adaptation for Action Recognition: A Disentanglement Perspective |
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Towards Label-Free Scene Understanding by Vision Foundation Models |
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Robo3D: Towards Robust and Reliable 3D Perception against Corruptions |
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Rethinking Range View Representation for LiDAR Segmentation |
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UniSeg: A Unified Multi-Modal LiDAR Segmentation Network and the OpenPCSeg Codebase |
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LaserMix for Semi-Supervised LiDAR Semantic Segmentation |
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CLIP2Scene: Towards Label-Efficient 3D Scene Understanding by CLIP |
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ConDA: Unsupervised Domain Adaptation for LiDAR Segmentation via Regularized Domain Concatenation |
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Benchmarking 3D Robustness to Common Corruptions and Sensor Failure |
Tech Reports
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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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Preprints
* equal contributions ‡ corresponding author
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Multi-Modal Data-Efficient 3D Scene Understanding for Autonomous Driving
arXiv, 2024
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Calib3D: Calibrating Model Preferences for Reliable 3D Scene Understanding
arXiv, 2024
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LargeAD: Large-Scale Cross-Sensor Data Pretraining for Autonomous Driving
arXiv, 2024
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Language-Drive Scene Understanding from Event Cameras
arXiv, 2024
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Is Your LiDAR Placement Optimized for 3D Scene Understanding in Autonomous Driving?
arXiv, 2024
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Is Your HD Map Constructor Reliable under Sensor Corruptions?
arXiv, 2024
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An Empirical Study of Training State-of-the-Art LiDAR Segmentation Models
arXiv, 2024
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Visual Foundation Models Boost Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation
arXiv, 2024
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Hierarchical Instance Feature Aggregation and Contrast for 3D Object Detection from Point Cloud Sequences
arXiv, 2024
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FRNet: Frustum-Range Networks for Scalable LiDAR SemanticSegmentation
arXiv, 2023
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PointCloud-C: Benchmarking and Analyzing Point Cloud Perception Robustness under Corruptions
arXiv, 2022
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Free Lunch for Co-Saliency Detection: Context Adjustment
arXiv, 2021
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Workshop Organizers
Venue: Yokohama, Japan
Industrial Experiences
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NVIDIA Research |
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OpenMMLab |
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Motional |
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ByteDance AI Lab |
Academic Services
Conference Reviewer
- IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- IEEE/CVF International Conference on Computer Vision (ICCV)
- European Conference on Computer Vision (ECCV)
- Conference on Neural Information Processing Systems (NeurIPS)
- International Conference on Learning Representations (ICLR)
- International Conference on Machine Learning (ICML)
- IEEE International Conference on Robotics and Automation (ICRA)
- IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Journal Reviewer
- International Journal of Computer Vision (IJCV)
- IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
- IEEE Transactions on Neural Networks and Learning Systems (TNNLS)
- IEEE Transactions on Intelligent Vehicles (TIV)
- IEEE Transactions on Circuits and Systems for Video Technology (TCSVT)
- IEEE Transactions on Knowledge and Data Engineering (TKDE)
- IEEE Robotics and Automation Letters (RA-L)
- ISPRS Journal of Photogrammetry and Remote Sensing (P&RS)