PointLAM: Local Attentive Mamba for Efficient Point-based 3D Object Detection

arXiv:2609.21780 · cs.CV · Submitted 2026-09-18 · Read on arXiv

cs.CV

Submitted: 2026-09-18

Updated: 2026-09-18

Code: https://github.com/open-mmlab/OpenPCDet

Project page: https://pointlam.github.io

Terminology

• Transformer — 20× in this paper · explained in 2 episodes
• Point Clouds — 18× in this paper · explained in 2 episodes
• Local Hadamard Aggregator — 10× in this paper
• Aggregation — 8× in this paper · explained in 1 episode
• Fidelity — 8× in this paper · explained in 2 episodes
• Laplacian Point Sampler — 8× in this paper
• Bi-Directional Mamba — 7× in this paper
• Local Attentive Mamba — 7× in this paper
• Robustness — 7× in this paper · explained in 5 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Accuracy — 6× in this paper · explained in 1 episode
• Benchmark — 4× in this paper · explained in 2 episodes
• Computational overhead — 4× in this paper · explained in 2 episodes
• Doubly Sorted Sampling — 4× in this paper
• Quantization — 4× in this paper · explained in 12 episodes
• Embeddings — 3× in this paper · explained in 2 episodes
• Feature Extraction — 3× in this paper · explained in 2 episodes
• Sparsity — 3× in this paper · explained in 4 episodes
• Abstraction — 2× in this paper · explained in 1 episode
• Class-Balanced Grouping and Sampling — 2× in this paper
• Core — 2× in this paper · explained in 2 episodes
• Farthest Point Sampling — 2× in this paper
• Representation Learning — 2× in this paper · explained in 2 episodes
• AI Foundation Model — 1× in this paper · explained in 1 episode
• Ablation Studies — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Gradient Flow — 1× in this paper · explained in 2 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Multimodal Dataset — 1× in this paper · explained in 2 episodes
• Point Feature Network — 1× in this paper
• Regularization — 1× in this paper · explained in 3 episodes

Sources

• Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection

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