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.
Marigold-dc: Zero-shot monocular depth completion with guided diffusion
4 Pith papers cite this work. Polarity classification is still indexing.
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cs.CV 4representative citing papers
LDCM achieves state-of-the-art metric depth completion from sparse observations by combining foundation-model initialization with a point-map regression head that removes the need for camera intrinsics.
Clear2Fog simulates fog on 270k Waymo images; mixed-density fog at 75% scale matches full fixed-density training performance, and adjusted learning rates improve sim-to-real transfer by up to 1.17 mAP.
citing papers explorer
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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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Large Depth Completion Model from Sparse Observations
LDCM achieves state-of-the-art metric depth completion from sparse observations by combining foundation-model initialization with a point-map regression head that removes the need for camera intrinsics.
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A Data Efficiency Study of Synthetic Fog for Object Detection Using the Clear2Fog Pipeline
Clear2Fog simulates fog on 270k Waymo images; mixed-density fog at 75% scale matches full fixed-density training performance, and adjusted learning rates improve sim-to-real transfer by up to 1.17 mAP.
- Radar-Guided Polynomial Fitting for Metric Depth Estimation