MM-TRELLIS extends TRELLIS with LiDAR point-cloud guidance and multi-view image conditioning plus voxel filtering to generate high-fidelity 3D vehicle meshes from in-the-wild driving data.
Enhanced diffusion sampling via extrap- olation with multiple ode solutions,
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FID improves with better samples only on concentrated reference datasets but can worsen on dispersed ones, as shown by density and effective rank in a controlled study across six datasets.
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MM-TRELLIS: Point-Cloud Guided Multi-Modal 3D Vehicle Generation in Autonomous Driving
MM-TRELLIS extends TRELLIS with LiDAR point-cloud guidance and multi-view image conditioning plus voxel filtering to generate high-fidelity 3D vehicle meshes from in-the-wild driving data.
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Rethinking FID Through the Geometry of the Reference Dataset
FID improves with better samples only on concentrated reference datasets but can worsen on dispersed ones, as shown by density and effective rank in a controlled study across six datasets.