GA-EIRFS: A Geometry-Augmented Repeat-Factor Sampling Method for Long-Tailed LiDAR 3D Object Detection

arXiv:2609.38116 · cs.CV · Submitted 2026-09-29 · Read on arXiv

cs.CV

Submitted: 2026-09-29

Updated: 2026-09-29

Code: https://github.com/Multimodal-Sensing-Lab/GA-EIRFS

Terminology

• Point Clouds — 5× in this paper · explained in 2 episodes
• Repeat Factor Sampling — 5× in this paper
• Conference on Computer Vision and Pattern Recognition — 3× in this paper
• Abstraction — 2× in this paper · explained in 1 episode
• Curricular Object Manipulation — 2× in this paper
• Data Augmentation — 2× in this paper · explained in 7 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Active Learning — 1× in this paper · explained in 6 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Adversarial Data Augmentation — 1× in this paper · explained in 1 episode
• Adversarial Examples — 1× in this paper · explained in 2 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Instance Segmentation — 1× in this paper · explained in 3 episodes
• Multimodal Fusion — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes

Sources

• Instance-Aware Repeat Factor Sampling for Long-Tailed Object Detection
• Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection
• Long-Tailed 3D Detection via Multi-Modal Fusion

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