FRUC enables one-shot calibration-free dynamic scene reconstruction from collaborative driving views via a geometric Transformer, ego-centric occlusion priors, and zero-initialized residual denoising, claiming SOTA quality and speed on V2XReal and UrbanIng-V2X.
Learning mutual view information graph for adaptive adversarial collaborative perception,
3 Pith papers cite this work. Polarity classification is still indexing.
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cs.CV 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
CoTIR fine-tunes a pre-trained image editing model using a differentiable CoT-style objective inspired by Lagrangian optimization to enable single-pass universal image restoration, supported by a new 5.2M-sample benchmark showing improved perceptual quality.
V2XCrafter introduces a progressive multi-agent diffusion model with cross-agent attention to generate controllable, consistent collaborative driving scenes for V2X data augmentation.
citing papers explorer
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FRUC: Feedforward Dynamic Scene Reconstruction from Uncalibrated Collaborative Driving Views
FRUC enables one-shot calibration-free dynamic scene reconstruction from collaborative driving views via a geometric Transformer, ego-centric occlusion priors, and zero-initialized residual denoising, claiming SOTA quality and speed on V2XReal and UrbanIng-V2X.
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Universal Image Restoration via Internalized Chain-of-Thought Reasoning
CoTIR fine-tunes a pre-trained image editing model using a differentiable CoT-style objective inspired by Lagrangian optimization to enable single-pass universal image restoration, supported by a new 5.2M-sample benchmark showing improved perceptual quality.
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V2XCrafter: Learning to Generate Driving Scene Across Agents
V2XCrafter introduces a progressive multi-agent diffusion model with cross-agent attention to generate controllable, consistent collaborative driving scenes for V2X data augmentation.