理想一篇中稿AAAI'26的LiDAR生成工作 - DriveLiDAR4D

Core Viewpoint - The article discusses the development of DriveLiDAR4D, a novel LiDAR scene generation pipeline by Li Auto, which integrates multimodal conditions and an innovative temporal noise prediction model, LiDAR4DNet, to generate temporally consistent LiDAR scenes with controllable foreground objects and realistic backgrounds [2][8]. Background Review - Data is a fundamental element driving AI development, especially in autonomous driving, where high-quality data is crucial due to the data-intensive nature of deep learning models and the need to capture rare driving behaviors and unique road environments [3]. - Current LiDAR scene generation methods have made significant progress but still face limitations, such as the inability to generate temporally consistent scenes and accurately positioned foreground objects [3][7]. DriveLiDAR4D Contributions - DriveLiDAR4D is the first end-to-end method to achieve temporal generation of LiDAR scenes with full scene control capabilities, featuring two core characteristics: integration of multimodal conditions and a carefully designed noise prediction model [8][9]. - The method allows for precise control over foreground objects and background elements, addressing the shortcomings of existing techniques that primarily focus on unconditional generation [7][8]. Methodology - The pipeline involves extracting three types of multimodal conditions (road sketches, scene descriptions, and object priors) during the training phase, which are then used to predict and reconstruct noisy image sequences [9][18]. - The LiDAR4DNet model employs an equirectangular representation for efficient scene description and integrates spatial-temporal convolution and transformer modules to enhance feature learning and maintain temporal consistency [18][20]. Experimental Results - DriveLiDAR4D outperforms state-of-the-art methods in generating LiDAR scenes, achieving a FRD score of 743.13 and an FVD score of 16.96 on the nuScenes dataset, with improvements of 37.2% and 24.1% respectively over the previous best method, UniScene [2][22][26]. - The model demonstrates significant advancements in both foreground and background control, as well as in the generation of temporally consistent sequences [22][30]. Conclusion - The introduction of DriveLiDAR4D marks a significant step forward in LiDAR scene generation for autonomous driving, providing a robust framework that enhances the realism and controllability of generated scenes, which is essential for the development of safe autonomous systems [2][8].