1 citations · 2 across the 5 of their papers we have counts for
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Native3D: End-to-End 3D Scene Generation via Unified Mesh-Texture Modeling and Semantic Alignment
Yibo Liu, Ziwei Zhang, Haozhou Pang +3
This paper presents Native3D, the first end-to-end 3D scene generation framework that completely bypasses 2D intermediate representations. Traditional approaches typically require…
HIPPo: Harnessing Image-to-3D Priors for Model-free Zero-shot 6D Pose Estimation
Yibo Liu, Zhaodong Jiang, Binbin Xu +7
This work focuses on model-free zero-shot 6D object pose estimation for robotics applications. While existing methods can estimate the precise 6D pose of objects, they heavily rely…
UniGaussian: Driving Scene Reconstruction from Multiple Camera Models via Unified Gaussian Representations
Yuan Ren, Guile Wu, Runhao Li +5
Urban scene reconstruction is crucial for real-world autonomous driving simulators. Although existing methods have achieved photorealistic reconstruction, they mostly focus on pinh…
VQA-Diff: Exploiting VQA and Diffusion for Zero-Shot Image-to-3D Vehicle Asset Generation in Autonomous Driving
Yibo Liu, Zheyuan Yang, Guile Wu +5
Generating 3D vehicle assets from in-the-wild observations is crucial to autonomous driving. Existing image-to-3D methods cannot well address this problem because they learn genera…
Learning Effective NeRFs and SDFs Representations with 3D Generative Adversarial Networks for 3D Object Generation
Zheyuan Yang, Yibo Liu, Guile Wu +4
We present a solution for 3D object generation of ICCV 2023 OmniObject3D Challenge. In recent years, 3D object generation has made great process and achieved promising results, but…
MV-DeepSDF: Implicit Modeling with Multi-Sweep Point Clouds for 3D Vehicle Reconstruction in Autonomous Driving
Yibo Liu, Kelly Zhu, Guile Wu +4
Reconstructing 3D vehicles from noisy and sparse partial point clouds is of great significance to autonomous driving. Most existing 3D reconstruction methods cannot be directly app…