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MM-TRELLIS generates 3D vehicles using multi-modal sensor data · 2 sources tracked

By PulseAugur Editorial · [2 sources] ·

Researchers have developed MM-TRELLIS, a novel method for generating realistic 3D vehicle models from autonomous driving data. This approach integrates multi-view images and LiDAR point clouds into native 3D generative models, addressing limitations of previous methods that struggled with in-the-wild data or produced low-quality meshes. MM-TRELLIS uses LiDAR point clouds for geometric accuracy and cross-view consistency, and a voxel filtering strategy with 3D Gaussian Splatting to refine meshes. Experiments on the Waymo dataset show superior performance in high-fidelity 3D vehicle generation. AI

IMPACT This research could improve the realism and efficiency of generating synthetic data for autonomous driving simulations.

RANK_REASON The cluster contains an academic paper detailing a new method for 3D vehicle generation.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

MM-TRELLIS generates 3D vehicles using multi-modal sensor data · 2 sources tracked

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The cluster contains an academic paper detailing a new method for 3D vehicle generation.
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COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Hongli Xiao, Youjian Zhang, Yucai Bai, Chaoyue Wang, Yaohui Jin, Xiaoguang Ren, Wenjing Yang, Long Lan ·

    MM-TRELLIS: Point-Cloud Guided Multi-Modal 3D Vehicle Generation in Autonomous Driving

    arXiv:2606.24301v1 Announce Type: new Abstract: Recovering realistic 3D vehicle models from autonomous driving scenes is crucial for synthesizing training data and building simulation environment. However, most existing vehicle generation methods fail to fully exploit multimodal …

  2. arXiv cs.CV TIER_1 English(EN) · Long Lan ·

    MM-TRELLIS: Point-Cloud Guided Multi-Modal 3D Vehicle Generation in Autonomous Driving

    Recovering realistic 3D vehicle models from autonomous driving scenes is crucial for synthesizing training data and building simulation environment. However, most existing vehicle generation methods fail to fully exploit multimodal sensors i.e. multi-view images and LiDAR point c…

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