1 citations · 1 across the 1 of their papers we have counts for
6 papers
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…