LiDAR-based world models offer more structured and geometry-aware representations than their image-based counterparts. However, existing LiDAR world models are narrowly trained; each model excels only in the domain for which it was built. Can we develop LiDAR world models that exhibit strong transferability across multiple domains? We conduct the first systematic domain transfer study across three demanding scenarios: (i) outdoor to indoor generalization, (ii) sparse-beam & dense-beam adaptation, and (iii) non-semantic to semantic transfer. Given different amounts of fine-tuning data, our experiments show that a single pre-trained model can achieve up to 11% absolute improvement (83% relative) over training from scratch and outperforms training from scratch in 30/36 of our comparisons. This transferability of dynamic learning significantly reduces the reliance on manually annotated data for semantic occupancy forecasting: our method exceed the previous semantic occupancy forecasting models with only 5% of the labeled training data required by prior models. We also observed inefficiencies of current LiDAR world models, mainly through their under-compression of LiDAR data and inefficient training objectives. To address this, we propose a latent conditional flow matching (CFM)-based frameworks that achieves state-of-the-art reconstruction accuracy using only half the training data and a compression ratio 6 times higher than that of prior methods. Our model achieves SOTA performance on future-trajectory-conditioned semantic occupancy forecasting while being 23x more computationally efficient (a 28x FPS speedup); and achieves SOTA performance on semantic occupancy forecasting while being 2x more computationally efficient (a 1.1x FPS speedup).

翻译：基于LiDAR的世界模型相较于基于图像的模型，能够提供更具结构化和几何感知能力的表示。然而，现有的LiDAR世界模型训练范围狭窄；每个模型仅在其构建的特定领域表现出色。我们能否开发出在多个领域均表现出强大可迁移性的LiDAR世界模型？我们首次在三个具有挑战性的场景中进行了系统的领域迁移研究：(i) 室外到室内的泛化，(ii) 稀疏波束与密集波束的适应，以及 (iii) 非语义到语义的迁移。在不同数量的微调数据下，我们的实验表明，单个预训练模型相比从头训练，可以实现高达11%的绝对提升（相对提升83%），并且在36次比较中的30次都优于从头训练。这种动态学习的可迁移性显著降低了对语义占据预测任务中手动标注数据的依赖：我们的方法仅需先前模型所需标注训练数据的5%，就超越了之前的语义占据预测模型。我们还观察到当前LiDAR世界模型的低效性，主要体现在其对LiDAR数据的压缩不足以及训练目标效率低下。为了解决这些问题，我们提出了一个基于潜在条件流匹配的框架，该框架仅使用一半的训练数据，并实现了比先前方法高6倍的压缩比，就达到了最先进的重建精度。我们的模型在基于未来轨迹条件的语义占据预测任务上达到了最先进的性能，同时计算效率提高了23倍（帧率提升了28倍）；在语义占据预测任务上也达到了最先进的性能，同时计算效率提高了2倍（帧率提升了1.1倍）。