7 citations · 20 across the 46 of their papers we have counts for
18 papers · 1 filter
Preventing Local Pitfalls in Vector Quantization via Optimal Transport
Borui Zhang, Wenzhao Zheng, Jie Zhou +1
Vector-quantized networks (VQNs) have exhibited remarkable performance across various tasks, yet they are prone to training instability, which complicates the training process due…
GaussianWorld: Gaussian World Model for Streaming 3D Occupancy Prediction
Sicheng Zuo, Wenzhao Zheng, Yuanhui Huang +2
3D occupancy prediction is important for autonomous driving due to its comprehensive perception of the surroundings. To incorporate sequential inputs, most existing methods fuse re…
Doe-1: Closed-Loop Autonomous Driving with Large World Model
Wenzhao Zheng, Zetian Xia, Yuanhui Huang +3
End-to-end autonomous driving has received increasing attention due to its potential to learn from large amounts of data. However, most existing methods are still open-loop and suf…
Owl-1: Omni World Model for Consistent Long Video Generation
Yuanhui Huang, Wenzhao Zheng, Yuan Gao +5
Video generation models (VGMs) have received extensive attention recently and serve as promising candidates for general-purpose large vision models. While they can only generate sh…
GPD-1: Generative Pre-training for Driving
Zixun Xie, Sicheng Zuo, Wenzhao Zheng +5
Modeling the evolutions of driving scenarios is important for the evaluation and decision-making of autonomous driving systems. Most existing methods focus on one aspect of scene e…
Stag-1: Towards Realistic 4D Driving Simulation with Video Generation Model
Lening Wang, Wenzhao Zheng, Dalong Du +8
4D driving simulation is essential for developing realistic autonomous driving simulators. Despite advancements in existing methods for generating driving scenes, significant chall…
Driv3R: Learning Dense 4D Reconstruction for Autonomous Driving
Xin Fei, Wenzhao Zheng, Yueqi Duan +4
Realtime 4D reconstruction for dynamic scenes remains a crucial challenge for autonomous driving perception. Most existing methods rely on depth estimation through self-supervision…
GaussianFormer-2: Probabilistic Gaussian Superposition for Efficient 3D Occupancy Prediction
Yuanhui Huang, Amonnut Thammatadatrakoon, Wenzhao Zheng +3
3D semantic occupancy prediction is an important task for robust vision-centric autonomous driving, which predicts fine-grained geometry and semantics of the surrounding scene. Mos…
EmbodiedOcc: Embodied 3D Occupancy Prediction for Vision-based Online Scene Understanding
Yuqi Wu, Wenzhao Zheng, Sicheng Zuo +3
3D occupancy prediction provides a comprehensive description of the surrounding scenes and has become an essential task for 3D perception. Most existing methods focus on offline pe…
PixelGaussian: Generalizable 3D Gaussian Reconstruction from Arbitrary Views
Xin Fei, Wenzhao Zheng, Yueqi Duan +4
We propose PixelGaussian, an efficient feed-forward framework for learning generalizable 3D Gaussian reconstruction from arbitrary views. Most existing methods rely on uniform pixe…
V2M: Visual 2-Dimensional Mamba for Image Representation Learning
Chengkun Wang, Wenzhao Zheng, Yuanhui Huang +2
Mamba has garnered widespread attention due to its flexible design and efficient hardware performance to process 1D sequences based on the state space model (SSM). Recent studies h…
GlobalMamba: Global Image Serialization for Vision Mamba
Chengkun Wang, Wenzhao Zheng, Jie Zhou +1
Vision mambas have demonstrated strong performance with linear complexity to the number of vision tokens. Their efficiency results from processing image tokens sequentially. Howeve…
OPONeRF: One-Point-One NeRF for Robust Neural Rendering
Yu Zheng, Yueqi Duan, Kangfu Zheng +3
In this paper, we propose a One-Point-One NeRF (OPONeRF) framework for robust scene rendering. Existing NeRFs are designed based on a key assumption that the target scene remains u…
OccSora: 4D Occupancy Generation Models as World Simulators for Autonomous Driving
Lening Wang, Wenzhao Zheng, Yilong Ren +4
Understanding the evolution of 3D scenes is important for effective autonomous driving. While conventional methods mode scene development with the motion of individual instances, w…
GaussianFormer: Scene as Gaussians for Vision-Based 3D Semantic Occupancy Prediction
Yuanhui Huang, Wenzhao Zheng, Yunpeng Zhang +2
3D semantic occupancy prediction aims to obtain 3D fine-grained geometry and semantics of the surrounding scene and is an important task for the robustness of vision-centric autono…
Hardness-Aware Scene Synthesis for Semi-Supervised 3D Object Detection
Shuai Zeng, Wenzhao Zheng, Jiwen Lu +1
3D object detection aims to recover the 3D information of concerning objects and serves as the fundamental task of autonomous driving perception. Its performance greatly depends on…
X-3D: Explicit 3D Structure Modeling for Point Cloud Recognition
Shuofeng Sun, Yongming Rao, Jiwen Lu +1
Numerous prior studies predominantly emphasize constructing relation vectors for individual neighborhood points and generating dynamic kernels for each vector and embedding these i…
Path Choice Matters for Clear Attribution in Path Methods
Borui Zhang, Wenzhao Zheng, Jie Zhou +1
Rigorousness and clarity are both essential for interpretations of DNNs to engender human trust. Path methods are commonly employed to generate rigorous attributions that satisfy t…