6 citations · 23 across the 12 of their papers we have counts for
12 papers
DriveTok: 3D Driving Scene Tokenization for Unified Multi-View Reconstruction and Understanding
Dong Zhuo, Wenzhao Zheng, Sicheng Zuo +4
With the growing adoption of vision-language-action models and world models in autonomous driving systems, scalable image tokenization becomes crucial as the interface for the visu…
DrivePI: Spatial-aware 4D MLLM for Unified Autonomous Driving Understanding, Perception, Prediction and Planning
Zhe Liu, Runhui Huang, Rui Yang +6
Although multi-modal large language models (MLLMs) have shown strong capabilities across diverse domains, their application in generating fine-grained 3D perception and prediction…
Wavelet-based Decoupling Framework for low-light Stereo Image Enhancement
Shuangli Du, Siming Yan, Zhenghao Shi +2
Low-light images suffer from complex degradation, and existing enhancement methods often encode all degradation factors within a single latent space. This leads to highly entangled…
ViGoR: Improving Visual Grounding of Large Vision Language Models with Fine-Grained Reward Modeling
Siming Yan, Min Bai, Weifeng Chen +3
By combining natural language understanding, generation capabilities, and breadth of knowledge of large language models with image perception, recent large vision language models (…
Multi-View Representation is What You Need for Point-Cloud Pre-Training
Siming Yan, Chen Song, Youkang Kong +1
A promising direction for pre-training 3D point clouds is to leverage the massive amount of data in 2D, whereas the domain gap between 2D and 3D creates a fundamental challenge. Th…
3D Feature Prediction for Masked-AutoEncoder-Based Point Cloud Pretraining
Siming Yan, Yuqi Yang, Yuxiao Guo +5
Masked autoencoders (MAE) have recently been introduced to 3D self-supervised pretraining for point clouds due to their great success in NLP and computer vision. Unlike MAEs used i…