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LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes

2025-01-07 18:59:58
Xiang Xu, Lingdong Kong, Hui Shuai, Liang Pan, Ziwei Liu, Qingshan Liu

Abstract

LiDAR data pretraining offers a promising approach to leveraging large-scale, readily available datasets for enhanced data utilization. However, existing methods predominantly focus on sparse voxel representation, overlooking the complementary attributes provided by other LiDAR representations. In this work, we propose LiMoE, a framework that integrates the Mixture of Experts (MoE) paradigm into LiDAR data representation learning to synergistically combine multiple representations, such as range images, sparse voxels, and raw points. Our approach consists of three stages: i) Image-to-LiDAR Pretraining, which transfers prior knowledge from images to point clouds across different representations; ii) Contrastive Mixture Learning (CML), which uses MoE to adaptively activate relevant attributes from each representation and distills these mixed features into a unified 3D network; iii) Semantic Mixture Supervision (SMS), which combines semantic logits from multiple representations to boost downstream segmentation performance. Extensive experiments across 11 large-scale LiDAR datasets demonstrate our effectiveness and superiority. The code and model checkpoints have been made publicly accessible.

Abstract (translated)

LiDAR数据预训练提供了一种有前景的方法,能够利用大规模且易于获取的数据集来提升数据利用率。然而,现有的方法主要侧重于稀疏体素表示,忽略了其他LiDAR表示所提供的互补属性。为此,我们提出了一个框架——LiMoE(LiDAR Mixture of Experts),它将专家混合(Mixture of Experts, MoE)范式融入到LiDAR数据表示学习中,以协同结合多种表示方式,如范围图像、稀疏体素和原始点云。 我们的方法分为三个阶段: 1. **Image-to-LiDAR 预训练**:这个阶段将先前的知识从图像转移到不同表示形式的点云上。 2. **对比混合学习(Contrastive Mixture Learning, CML)**:该阶段使用MoE自适应激活每种表示的相关属性,并将这些混合特征提炼到一个统一的3D网络中。 3. **语义混合监督(Semantic Mixture Supervision, SMS)**:该阶段结合多种表示方式中的语义逻辑以增强下游分割任务的表现。 在11个大规模LiDAR数据集上进行的广泛实验展示了我们方法的有效性和优越性。代码和模型检查点已公开提供。

URL

https://arxiv.org/abs/2501.04004

PDF

https://arxiv.org/pdf/2501.04004.pdf


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