MonoLaDN: Language-Guided DeNoising for Monocular 3D Object Detection

MonoLaDN introduces a language-guided denoising framework that transfers geometric reasoning from a frozen vision‑language model (VLM) into a monocular 3D object detector. During training, the VLM generates structured reasoning about occlusion, depth, dimension, and angle for each object, which is encoded into queries, perturbed with noise, and used to train the detector to reconstruct the clean reasoning. This process embeds the VLM’s geometric priors into the detector’s depth‑guided features, allowing the deployed model to remain purely vision‑based with no added latency or parameters, and achieves state‑of‑the‑art performance on KITTI 3D and nuScenes while generalizing well to new domains.

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• llms
• computer-vision
• multimodal
• benchmarks