LiDARDraft: Generating LiDAR Point Cloud from Versatile Inputs

• URL: http://arxiv.org/abs/2512.20105v1
• Date: Tue, 23 Dec 2025 07:03:31 GMT
• Title: LiDARDraft: Generating LiDAR Point Cloud from Versatile Inputs
• Authors: Haiyun Wei, Fan Lu, Yunwei Zhu, Zehan Zheng, Weiyi Xue, Lin Shao, Xudong Zhang, Ya Wu, Rong Fu, Guang Chen,
• Abstract summary: We propose LiDARDraft to generate realistic and diverse LiDAR point clouds.<n>The 3D layout can be trivially generated from various user inputs.<n>We employ a rangemap-based ControlNet to guide LiDAR point cloud generation.
• Score: 16.062937048950946
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generating realistic and diverse LiDAR point clouds is crucial for autonomous driving simulation. Although previous methods achieve LiDAR point cloud generation from user inputs, they struggle to attain high-quality results while enabling versatile controllability, due to the imbalance between the complex distribution of LiDAR point clouds and the simple control signals. To address the limitation, we propose LiDARDraft, which utilizes the 3D layout to build a bridge between versatile conditional signals and LiDAR point clouds. The 3D layout can be trivially generated from various user inputs such as textual descriptions and images. Specifically, we represent text, images, and point clouds as unified 3D layouts, which are further transformed into semantic and depth control signals. Then, we employ a rangemap-based ControlNet to guide LiDAR point cloud generation. This pixel-level alignment approach demonstrates excellent performance in controllable LiDAR point clouds generation, enabling "simulation from scratch", allowing self-driving environments to be created from arbitrary textual descriptions, images and sketches.

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