MMLab@NTU
Multimedia Laboratory @
Nanyang Technological University
Affiliated with S-Lab
About
MMLab@NTU
MMLab@NTU was formed on the 1 August 2018, with a research focus on computer vision and deep learning. Its sister lab is MMLab@CUHK. It is now a group with three faculty members and more than 40 members including research fellows, research assistants, research engineers and PhD students.
Members in MMLab@NTU conduct research primarily in low-level vision, image and video understanding, and generative AI. Have a look at the overview of our research. All publications are listed here.
We are always looking for motivated PhD students, postdocs, research assistants who have the same interests like us. Check out the careers page and follow us on Twitter.
In Memoriam: Xiaoou Tang
05/2024: The Editors-in-Chief of the International Journal of Computer Vision (IJCV) are deeply saddened by the significant loss of Xiaoou Tang, a former Editor-in-Chief of IJCV, who passed away on 15 December 2023 at the age of 55.
Asian Young Scientist Fellowship
08/2024: Ziwei Liu is awarded the Asian Young Scientist Fellowship. The AYSF committee acknowledges his contribution to the development of AI-driven mixed reality and would like to support his exploration in the direction of high-quality reconstruction for virtual/mixed reality toward advancing AI theories, algorithms, and systems for holistic perceptions.
Check Out
News and Highlights
- 12/2023: Congratulations to Mingyuan Zhang, Ruicheng Feng, and Jiawei Ren for being selected as top reviewers of NeurIPS 2023.
- 11/2023: Ziwei Liu has been recognized by MIT Technology Review: Innovators under 35 Asia Pacific 2023 — an incredible achievement. Congratulations to Ziwei!
- 07/2023: The team has a total of 14 papers (including four spotlight papers and two under the 'Datasets and Benchmarks' track) accepted to NeurIPS 2023.
- 07/2023: The team has a total of 20 papers accepted to ICCV 2023.
- 05/2023: Congratulations to Kelvin Chan for winning the SCSE Outstanding PhD Thesis Award 2023!
- 05/2023: Chongyi Li, Fangzhou Hong, Henghui Ding, Jiahao Xie and Shuai Yang are selected as outstanding reviewers of CVPR 2023. Congrats!
- 04/2023: Zhaoxi Chen is awarded Meta PhD Fellowship Finalist 2023. Congrats!
CVPR 2024 Workshop
Mobile Intelligent Photography and Imaging
Here comes the Third Workshop of Mobile Intelligent Photography and Imaging, held in conjunction of CVPR 2024. There will be three challenge tracks, namely few-shot raw image denoising, demosaic for hybridevs camera, and nighttime flare removal. We also invite submission of workshop papers. Check the site for more information.
- Paper submission deadline: March 10, 2024
Recent
Projects
OmniObject3D: Large-Vocabulary 3D Object Dataset for Realistic Perception, Reconstruction and Generation
T. Wu, J. Zhang, X. Fu, Y. Wang, J. Ren, L. Pan, W. Wu, L. Yang, J. Wang, C. Qian, D. Lin, Z. Liu
in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2023 (CVPR, Award Candidate)
[PDF]
[arXiv]
[Supplementary Material]
[Project Page]
We propose OmniObject3D, a large vocabulary 3D object dataset with massive high-quality real-scanned 3D objects to facilitate the development of 3D perception, reconstruction, and generation in the real world. OmniObject3D comprises 6,000 scanned objects in 190 daily categories, sharing common classes with popular 2D datasets (e.g., ImageNet and LVIS), benefiting the pursuit of generalizable 3D representations.
F2-NeRF: Fast Neural Radiance Field Training with Free Camera Trajectories
P. Wang, Y. Liu, Z. Chen, L. Liu, Z. Liu, T. Komura, C. Theobalt, W. Wang
in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2023 (CVPR, Highlight)
[PDF]
[arXiv]
[Supplementary Material]
[Project Page]
This paper presents a novel grid-based NeRF called F2-NeRF (Fast-Free-NeRF) for novel view synthesis, which enables arbitrary input camera trajectories and only costs a few minutes for training. Existing two widely-used space-warping methods are only designed for the forward-facing trajectory or the 360 degree object-centric trajectory but cannot process arbitrary trajectories. In this paper, we delve deep into the mechanism of space warping to handle unbounded scenes.
LaserMix for Semi-Supervised LiDAR Semantic Segmentation
L. Kong, J. Ren, L. Pan, Z. Liu
in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2023 (CVPR, Highlight)
[PDF]
[arXiv]
[Supplementary Material]
[Project Page]
We study the underexplored semi-supervised learning problem in LiDAR segmentation. Our core idea is to leverage the strong spatial cues of LiDAR point clouds to better exploit unlabeled data. We propose LaserMix to mix laser beams from different LiDAR scans, and then encourage the model to make consistent and confident predictions before and after mixing.
Nighttime Smartphone Reflective Flare Removal using Optical Center Symmetry Prior
Y. Dai, Y. Luo, S. Zhou, C. Li, C. C. Loy
in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2023 (CVPR, Highlight)
[PDF]
[arXiv]
[Supplementary Material]
[Project Page]
Reflective flare is a phenomenon that occurs when light reflects inside lenses, causing bright spots or a “ghosting effect” in photos. we propose an optical center symmetry prior, which suggests that the reflective flare and light source are always symmetrical around the lens’s optical center. This prior helps to locate the reflective flare’s proposal region more accurately and can be applied to most smartphone cameras.
