3 citations · 8 across the 17 of their papers we have counts for
17 papers
TurboVGGT: Fast Visual Geometry Reconstruction with Adaptive Alternating Attention
David Huang, Guile Wu, Chengjie Huang +2
Recent feed-forward 3D reconstruction methods, such as visual geometry transformers, have substantially advanced the traditional per-scene optimization paradigm by enabling effecti…
Part-Level 3D Gaussian Vehicle Generation with Joint and Hinge Axis Estimation
Shiyao Qian, Yuan Ren, Dongfeng Bai +1
Simulation is essential for autonomous driving, yet current frameworks often model vehicles as rigid assets and fail to capture part-level articulation. With perception algorithms…
FreeFix: Boosting 3D Gaussian Splatting via Fine-Tuning-Free Diffusion Models
Hongyu Zhou, Zisen Shao, Sheng Miao +4
Neural Radiance Fields and 3D Gaussian Splatting have advanced novel view synthesis, yet still rely on dense inputs and often degrade at extrapolated views. Recent approaches lever…
EVolSplat4D: Efficient Volume-based Gaussian Splatting for 4D Urban Scene Synthesis
Sheng Miao, Sijin Li, Pan Wang +5
Novel view synthesis (NVS) of static and dynamic urban scenes is essential for autonomous driving simulation, yet existing methods often struggle to balance reconstruction time wit…
MoVieDrive: Urban Scene Synthesis with Multi-Modal Multi-View Video Diffusion Transformer
Guile Wu, David Huang, Dongfeng Bai +1
Urban scene synthesis with video generation models has recently shown great potential for autonomous driving. Existing video generation approaches to autonomous driving primarily f…
ArmGS: Composite Gaussian Appearance Refinement for Modeling Dynamic Urban Environments
Guile Wu, Dongfeng Bai, Bingbing Liu
This work focuses on modeling dynamic urban environments for autonomous driving simulation. Contemporary data-driven methods using neural radiance fields have achieved photorealist…
EVolSplat: Efficient Volume-based Gaussian Splatting for Urban View Synthesis
Sheng Miao, Jiaxin Huang, Dongfeng Bai +6
Novel view synthesis of urban scenes is essential for autonomous driving-related applications.Existing NeRF and 3DGS-based methods show promising results in achieving photorealisti…
An Efficient Occupancy World Model via Decoupled Dynamic Flow and Image-assisted Training
Haiming Zhang, Ying Xue, Xu Yan +6
The field of autonomous driving is experiencing a surge of interest in world models, which aim to predict potential future scenarios based on historical observations. In this paper…
HUGSIM: A Real-Time, Photo-Realistic and Closed-Loop Simulator for Autonomous Driving
Hongyu Zhou, Longzhong Lin, Jiabao Wang +6
In the past few decades, autonomous driving algorithms have made significant progress in perception, planning, and control. However, evaluating individual components does not fully…
D-World: An Efficient World Model through Decoupled Dynamic Flow
Haiming Zhang, Xu Yan, Ying Xue +4
This technical report summarizes the second-place solution for the Predictive World Model Challenge held at the CVPR-2024 Workshop on Foundation Models for Autonomous Systems. We i…
VisionPAD: A Vision-Centric Pre-training Paradigm for Autonomous Driving
Haiming Zhang, Wending Zhou, Yiyao Zhu +7
This paper introduces VisionPAD, a novel self-supervised pre-training paradigm designed for vision-centric algorithms in autonomous driving. In contrast to previous approaches that…
AutoSplat: Constrained Gaussian Splatting for Autonomous Driving Scene Reconstruction
Mustafa Khan, Hamidreza Fazlali, Dhruv Sharma +4
Realistic scene reconstruction and view synthesis are essential for advancing autonomous driving systems by simulating safety-critical scenarios. 3D Gaussian Splatting excels in re…
Efficient Depth-Guided Urban View Synthesis
Sheng Miao, Jiaxin Huang, Dongfeng Bai +4
Recent advances in implicit scene representation enable high-fidelity street view novel view synthesis. However, existing methods optimize a neural radiance field for each scene, r…
Neural Radiance Fields with Torch Units
Bingnan Ni, Huanyu Wang, Dongfeng Bai +4
Neural Radiance Fields (NeRF) give rise to learning-based 3D reconstruction methods widely used in industrial applications. Although prevalent methods achieve considerable improvem…
HUGS: Holistic Urban 3D Scene Understanding via Gaussian Splatting
Hongyu Zhou, Jiahao Shao, Lu Xu +6
Holistic understanding of urban scenes based on RGB images is a challenging yet important problem. It encompasses understanding both the geometry and appearance to enable novel vie…
Forging Vision Foundation Models for Autonomous Driving: Challenges, Methodologies, and Opportunities
Xu Yan, Haiming Zhang, Yingjie Cai +13
The rise of large foundation models, trained on extensive datasets, is revolutionizing the field of AI. Models such as SAM, DALL-E2, and GPT-4 showcase their adaptability by extrac…
RadOcc: Learning Cross-Modality Occupancy Knowledge through Rendering Assisted Distillation
Haiming Zhang, Xu Yan, Dongfeng Bai +5
3D occupancy prediction is an emerging task that aims to estimate the occupancy states and semantics of 3D scenes using multi-view images. However, image-based scene perception enc…