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

Paper recorded by Signals 4 on 2026-09-18 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-18 on arXiv · recorded by Signals 4 on 2026-09-21

Category: cs.CV · 计算机视觉 · first seen 2026-09-21

Abstract

3D object detection from LiDAR point clouds faces a fundamental dilemma: voxel-based methods achieve efficiency at the cost of geometric quantization, while point-based methods preserve fidelity but suffer from prohibitive computational bottlenecks. Specifically, point-based architectures are crippled by slow downsampling strategies (e.g., FPS) and expensive dynamic neighbor queries (e.g., k-NN) c

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#33 most recent of 270 cs.CV papers we have recorded · ↑ newer: A Principled Approach to Unsupervised Anomaly Detection · ↓ older: Can 4D Foundation Models Remember?
Cite this page: PointLAM: Local Attentive Mamba for Efficient Point-based 3D Object Detection: the #33 most recent of 270 cs.CV papers we have recorded (as of 2026-09-18). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/pointlam-local-attentive-mamba-for-efficient-point-based-3d-object-detection.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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