5 papers
Rethinking Driving World Model as Synthetic Data Generator for Perception Tasks
Kai Zeng, Zhanqian Wu, Kaixin Xiong +12
Recent advancements in driving world models enable controllable generation of high-quality RGB videos or multimodal videos. Existing methods primarily focus on metrics related to g…
CoGen: 3D Consistent Video Generation via Adaptive Conditioning for Autonomous Driving
Yishen Ji, Ziyue Zhu, Zhenxin Zhu +7
Recent progress in driving video generation has shown significant potential for enhancing self-driving systems by providing scalable and controllable training data. Although pretra…
CMamba: Learned Image Compression with State Space Models
Zhuojie Wu, Heming Du, Shuyun Wang +4
Learned Image Compression (LIC) has explored various architectures, such as Convolutional Neural Networks (CNNs) and transformers, in modeling image content distributions in order…
DreamCar: Leveraging Car-specific Prior for in-the-wild 3D Car Reconstruction
Xiaobiao Du, Haiyang Sun, Ming Lu +2
Self-driving industries usually employ professional artists to build exquisite 3D cars. However, it is expensive to craft large-scale digital assets. Since there are already numero…
3DRealCar: An In-the-wild RGB-D Car Dataset with 360-degree Views
Xiaobiao Du, Yida Wang, Haiyang Sun +8
3D cars are commonly used in self-driving systems, virtual/augmented reality, and games. However, existing 3D car datasets are either synthetic or low-quality, limiting their appli…