DCDA uses 4D radar-conditioned diffusion with dual critics to align degraded LiDAR features to a clean manifold, enabling generalization to unseen weather types and severities without paired data or labels.
arXiv preprint arXiv:2505.09422 (2025)
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2verdicts
UNVERDICTED 2representative citing papers
A survey synthesizing sensor fusion strategies, AV datasets, and emerging LLM/VLM-powered object detection pipelines for autonomous vehicles.
citing papers explorer
-
Open-Weather Robust 3D Detection via Dual-Critic Diffusion Alignment
DCDA uses 4D radar-conditioned diffusion with dual critics to align degraded LiDAR features to a clean manifold, enabling generalization to unseen weather types and severities without paired data or labels.
-
All You Need for Object Detection: From Pixels, Points, and Prompts to Next-Gen Fusion and Multimodal LLMs/VLMs in Autonomous Vehicles
A survey synthesizing sensor fusion strategies, AV datasets, and emerging LLM/VLM-powered object detection pipelines for autonomous vehicles.