LiDAR point cloud data is indispensable for autonomous driving systems, enabling critical 3D perception tasks such as 3D object detection and segmentation. However, a shortage of labeled LiDAR data hinders the development of robust deep learning algorithms for these tasks. Data augmentation, as an important and effective technique to increase labeled data, has been applied to LiDAR data in different ways such as geometric transformation, mixup, and inserting synthetic objects. In this paper, we focus on exploring how to utilize synthetic objects to augment LiDAR data in a more effective manner. Given synthetic objects which are in the form of CAD models, we consider three factors that significantly impact the realism and utility of augmented data: insertion pose, point cloud spatial distribution, and point intensity value. Unlike previous insertion methods which only addressed one or two of these factors, our novel framework, LPA Aug, learns to place synthetic objects in and adjust them to existing scenes. A pose prediction module first learns to generate object placement locations and orientations based on scene context. Then, a novel 2D image-based distribution adjustment module adjusts the spatial distribution of points sampled from the inserted synthetic object. Finally, a further intensity prediction module learns in a 2D manner to predict the intensity of each object point. We evaluate LPA-Aug by using the augmented data for 3D object detection on the KITTI dataset. The results demonstrate the superiority of LPA-Aug over prior methods of LiDAR data augmentation.

Zhengren Wang, Qinhan Yu, Shida Wei, Zhiyu Li, Feiyu Xiong, Xiaoxing Wang, Simin Niu, Hao Liang, Wentao Zhang. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Abstract In this paper, we address the problem of plausible object placement for the challenging task of realistic image composition. We propose DiffPop, the first framework that utilizes plausibility‐guided denoising diffusion probabilistic model to learn the scale and spatial relations among multiple objects and the corresponding scene image. First, we train an unguided diffusion model to directly learn the object placement parameters in a self‐supervised manner. Then, we develop a human‐in‐the‐loop pipeline which exploits human labeling on the diffusion‐generated composite images to provide the weak supervision for training a structural plausibility classifier. The classifier is further used to guide the diffusion sampling process towards generating the plausible object placement. Experimental results verify the superiority of our method for producing plausible and diverse composite images on the new Cityscapes‐OP dataset and the public OPA dataset, as well as demonstrate its potential in applications such as data augmentation and multi‐object placement tasks. Our dataset and code will be released.