GEM: 通过可变形Mamba生成LiDAR世界模型
GEM: Generating LiDAR World Model via Deformable Mamba
AI总结：
GEM通过可变形Mamba架构提升LiDAR世界模型的逼真度与想象力，解决点云无序和动态静态区分难题，实现空间时间感知与自主探索能力。
摘要：
世界模型通过模拟环境动态和生成传感器观测，在自动驾驶中日益受到关注。然而，基于LiDAR的世界模型进展滞后于基于摄像机视频或占用数据的模型，主要由于LiDAR点云的固有无序性和动态物体与静态结构区分的困难。为此，我们提出GEM：一种生成式LiDAR世界模型，利用可变形Mamba架构显著提升保真度和想象力。具体而言，利用序列激光扫描与Mamba处理机制的结构相似性，我们首先通过定制的LiDAR场景分词器将LiDAR扫描转换为紧凑表示。随后通过动态-静态分离器对分词特征进行无监督解耦，引入三路径可变形Mamba以在解耦特征上执行选择性扫描和自适应门控融合，从而增强对世界演化的空间-时间理解。可选地，规划器和BEV布局控制器可集成以探索模型的自主展开能力及其生成『what-if』场景的潜力。大量实验表明，GEM在多种基准和评估设置中均取得最先进的性能，证明其优越性和有效性。项目页面：https://github.com/wuyang98/GEM。
英文摘要：
World models, which simulate environmental dynamics and generate sensor observations, are gaining increasing attention in autonomous driving. However, progress in LiDAR-based world models has lagged behind those built on camera videos or occupancy data, primarily due to two core challenges: the inherent disorder of LiDAR point clouds and the difficulty of distinguishing dynamic objects from static structures. To address these issues, we propose GEM: a Generative LiDAR world model that leverages deformable mamba architecture, significantly improving fidelity and imaginative capability. Specifically, leveraging the structural similarity between sequential laser scanning and Mamba's processing mechanism, we first tokenize LiDAR sweeps into compact representations via a custom LiDAR scene tokenizer. After unsupervised disentanglement of tokenized features via a dynamic-static separator, a tri-path deformable Mamba is introduced to perform selective scanning and adaptive gating fusion over the disentangled features, leading to enhanced spatial-temporal understanding of the world evolution. Optionally, a planner and a BEV layout controller can be integrated to explore the model's capability for autonomous rollout and its potential to generate ``what-if" scenarios. Extensive experiments show that GEM achieves state-of-the-art performances across diverse benchmarks and evaluation settings, demonstrating its superiority and effectiveness. Project page: https://github.com/wuyang98/GEM.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
