Towards robust multimodal 3D object detection via visual foundation models

arXiv:2609.23541 · cs.CV, cs.RO · Submitted 2026-09-20 · Read on arXiv

cs.CV, cs.RO

Submitted: 2026-09-20

Updated: 2026-09-20

Code: https://github.com/thu-ml/3D_Corruptions_AD

Terminology

• Robustness — 35× in this paper · explained in 5 episodes
• Conference on Computer Vision and Pattern Recognition — 32× in this paper
• Alignment — 26× in this paper · explained in 1 episode
• Transformer — 24× in this paper · explained in 2 episodes
• Generalization — 23× in this paper · explained in 13 episodes
• Foundation Model — 22× in this paper · explained in 3 episodes
• Distillation — 19× in this paper · explained in 2 episodes
• Foundation Models — 19× in this paper · explained in 7 episodes
• Adaptation — 12× in this paper · explained in 1 episode
• Domain Adaptation — 12× in this paper · explained in 5 episodes
• CVF International Conference on Computer Vision — 8× in this paper
• Ablation — 6× in this paper · explained in 1 episode
• Benchmark — 6× in this paper · explained in 2 episodes
• Embeddings — 5× in this paper · explained in 2 episodes
• Knowledge Distillation — 5× in this paper · explained in 11 episodes
• Point Clouds — 5× in this paper · explained in 2 episodes
• Accuracy — 4× in this paper · explained in 1 episode
• Aggregation — 4× in this paper · explained in 1 episode
• IEEE International Conference on Robotics and Automation — 4× in this paper
• Multimodal Fusion — 4× in this paper · explained in 3 episodes
• Segment Anything Model — 4× in this paper
• Feature Extraction — 3× in this paper · explained in 2 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Adaptability — 2× in this paper · explained in 1 episode
• Benchmarking — 2× in this paper · explained in 4 episodes
• Contrastive Learning — 2× in this paper · explained in 5 episodes
• Distribution Shift — 2× in this paper · explained in 6 episodes
• Multimodal Dataset — 2× in this paper · explained in 2 episodes
• Segment Anything Model (SAM) — 2× in this paper · explained in 2 episodes
• Vision Transformer (ViT) — 2× in this paper · explained in 2 episodes
• Wavelet Transform — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Ablation study — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Data Heterogeneity — 1× in this paper · explained in 3 episodes
• Depth-Guided Wavelet Attention Module — 1× in this paper
• Domain Shift — 1× in this paper · explained in 4 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Multimodal Integration — 1× in this paper · explained in 2 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Semantic Drift — 1× in this paper · explained in 3 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes

Sources

• ContrastAlign: Toward Robust BEV Feature Alignment via Contrastive Learning for Multi-Modal 3D Object Detection
• Faster Segment Anything: Towards Lightweight SAM for Mobile Applications

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