4 citations · 6 across the 7 of their papers we have counts for
11 papers
Point as Skeleton: Accumulated Point Cloud Enhanced Autoregressive Generation for Closed-Loop Autonomous Driving Simulation
Songbur Wong, Xiaosong Jia, Junqi You +12
Evaluating end-to-end autonomous driving (E2E-AD) remains challenging, as existing driving simulation methods often trade off closed-loop interactivity (e.g., CARLA) and real-world…
UniUGG: Unified 3D Understanding and Generation via Geometric-Semantic Encoding
Yueming Xu, Jiahui Zhang, Ze Huang +12
Despite the impressive progress on understanding and generating images shown by the recent unified architectures, the integration of 3D tasks remains challenging and largely unexpl…
BézierGS: Dynamic Urban Scene Reconstruction with Bézier Curve Gaussian Splatting
Zipei Ma, Junzhe Jiang, Yurui Chen +1
The realistic reconstruction of street scenes is critical for developing real-world simulators in autonomous driving. Most existing methods rely on object pose annotations, using t…
4D-VLA: Spatiotemporal Vision-Language-Action Pretraining with Cross-Scene Calibration
Jiahui Zhang, Yurui Chen, Yueming Xu +8
Leveraging diverse robotic data for pretraining remains a critical challenge. Existing methods typically model the dataset's action distribution using simple observations as inputs…
From Flatland to Space: Teaching Vision-Language Models to Perceive and Reason in 3D
Jiahui Zhang, Yurui Chen, Yanpeng Zhou +10
Recent advances in LVLMs have improved vision-language understanding, but they still struggle with spatial perception, limiting their ability to reason about complex 3D scenes. Unl…
BANet: Bilateral Aggregation Network for Mobile Stereo Matching
Gangwei Xu, Jiaxin Liu, Xianqi Wang +5
State-of-the-art stereo matching methods typically use costly 3D convolutions to aggregate a full cost volume, but their computational demands make mobile deployment challenging. D…
GS-LiDAR: Generating Realistic LiDAR Point Clouds with Panoramic Gaussian Splatting
Junzhe Jiang, Chun Gu, Yurui Chen +1
LiDAR novel view synthesis (NVS) has emerged as a novel task within LiDAR simulation, offering valuable simulated point cloud data from novel viewpoints to aid in autonomous drivin…
ZeroStereo: Zero-shot Stereo Matching from Single Images
Xianqi Wang, Hao Yang, Gangwei Xu +6
State-of-the-art supervised stereo matching methods have achieved remarkable performance on various benchmarks. However, their generalization to real-world scenarios remains challe…
S-NeRF++: Autonomous Driving Simulation via Neural Reconstruction and Generation
Yurui Chen, Junge Zhang, Ziyang Xie +4
Autonomous driving simulation system plays a crucial role in enhancing self-driving data and simulating complex and rare traffic scenarios, ensuring navigation safety. However, tra…
Periodic Vibration Gaussian: Dynamic Urban Scene Reconstruction and Real-time Rendering
Yurui Chen, Chun Gu, Junzhe Jiang +2
Modeling dynamic, large-scale urban scenes is challenging due to their highly intricate geometric structures and unconstrained dynamics in both space and time. Prior methods often…
Single-view Neural Radiance Fields with Depth Teacher
Yurui Chen, Chun Gu, Feihu Zhang +1
Neural Radiance Fields (NeRF) have been proposed for photorealistic novel view rendering. However, it requires many different views of one scene for training. Moreover, it has poor…