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李高斯Occ:完全自主监督的3D语义占用预测从LiDAR通过高斯喷涂

Zhiqiang Wei¹, Tao Huang², Fengdeng Zhang¹

¹School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.

Sensors (Basel, Switzerland)

|September 27, 2025

概括

LiGaussOcc是一种使用LiDAR数据进行3D语义占用感知的新自主监督方法. 它在没有手动3D注释的情况下实现了竞争性性能,为自动驾驶提供了一个可扩展的,无注释的解决方案.

科学领域:

计算机视觉计算机视觉
机器人技术机器人技术机器人技术
自主系统自主系统

背景情况:

准确的3D语义占用感知对于自动驾驶至关重要,特别是在复杂的环境中.
现有的基于视觉的方法在深度和照明方面扎,而LiDAR方法面临稀疏性和注释挑战.

研究的目的:

介绍LiGaussOcc,一个新的自我监督框架,用于基于LiDAR的密集3D语义占用预测.
为了消除在基于LiDAR的占用感知中需要昂贵的手动3D注释.

主要方法:

通过 Empty Voxel Inpainting (EVI) 和自适应特征融合 (AFF) 模块将LiDAR点云编码为voxel特征.
使用Gaussian Primitive from Voxels (GPV) 模块进行3D Gaussian Splatting以染2D语义和深度地图.
在自主监督培训中使用光度一致性和视觉语言模型伪标签.

主要成果:

在nuScenes-OpenOccupancy基准指标上取得了竞争性表现,30.4%的IOU和14.1%的mIoU.
在没有手动3D注释的情况下,达到91.6%的完全监督的L-CONet性能.
在可驱动的表面和结构等静态环境类别中表现出强的性能.

结论:

LiGaussOcc提供了一个可扩展和无注释的解决方案,用于使用LiDAR进行3D语义占用感知.
自主监督的方法显著降低了与3D数据注释相关的成本和劳动力.
这种方法提高了在非结构化的环境中自主导航系统的稳定性.

关键词:

三维语义占用3D语义占用高斯的染方式是高斯的染方式.激光雷达 (LiDAR) 的感知方式自动驾驶自动驾驶的自动驾驶.一个点云,一个点云.自己监督的自我监督.发音化发音化发音化

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