3D object detection in adverse weather is crucial for autonomous driving, especially in smoke where LiDAR data becomes sparse and noisy. Due to the lack of real smoke data, this paper introduces a physics-based simulation framework to generate realistic LiDAR point clouds of smoke and augment large-scale driving datasets. First, we present a 3D fluid dynamics-based smoke simulation framework in Unity, which models the realistic spatial diffusion and temporal evolution of smoke particles. Coupled with a physically accurate LiDAR perception module, our system captures complex light interactions—such as beam attenuation, scattering, and multi-path effects—to generate high-fidelity, physically consistent smoke point clouds. Second, we propose a range image-based data fusion strategy that seamlessly integrates the simulated smoke point clouds into large-scale real-world LiDAR datasets (e.g., Waymo). This approach accurately emulates LiDAR scanning characteristics and naturally incorporates occlusion effects, enabling realistic smoke integration without compromising spatial consistency. To validate our approach, we collect a real-world LiDAR smoke dataset (LiSmoke) and conduct extensive experiments using state-of-the-art 3D detectors. Results demonstrate that models trained with our augmented synthetic data achieve significant improvements in smoke-affected scenarios, while maintaining competitive performance in clear-weather conditions. Our work provides a cost-effective solution for enhancing perception robustness in safety-critical environments.

Autonomous vehicles (AVs) rely on LiDAR sensors for environmental perception and decision-making in driving scenarios. However, ensuring the safety and reliability of AVs in complex environments remains a pressing challenge. To address this issue, we introduce a real-world dataset (ROLiD) comprising LiDAR-scanned point clouds of two random objects: water mist and smoke. In this paper, we introduce a novel adversarial perspective by proposing an attack framework that utilizes water mist and smoke to simulate environmental interference. Specifically, we propose a point cloud sequence generation method using a motion and content decomposition generative adversarial network named PCS-GAN to simulate the distribution of random objects. Furthermore, leveraging the simulated LiDAR scanning characteristics implemented with Range Image, we examine the effects of introducing random object perturbations at various positions on the target vehicle. Extensive experiments demonstrate that adversarial perturbations based on random objects effectively deceive vehicle detection and reduce the recognition rate of 3D object detection models.

Adversarial examples can mislead deep neural networks with subtle perturbations, causing them to make incorrect predictions. Notably, adversarial examples crafted for one model can also deceive other models, a phenomenon known as the transferability of adversarial examples. To improve transferability, existing studies have designed increasingly complex mechanisms, but the improvements achieved remain relatively limited and are often difficult to adapt to other modalities, further restricting the scalability of these methods. In this work, we observe a mirroring relationship between model generalization and adversarial example transferability. Motivated by this observation, we propose an augmentation-based attack, called OPS (OperatorPerturbation-based Stochastic optimization), which constructs a stochastic optimization problem by input transformation operators and random perturbations, and solves this problem to generate adversarial examples with better transferability. Extensive experiments on both images and 3D point clouds demonstrate that OPS significantly outperforms existing state-of-the-art methods in terms of both performance and cost, showcasing the universality and superiority of our approach. The code is available at https://github.com/the-full/OPS.

Although 3D point cloud classification neural network models have been widely used, the in-depth interpretation of the activation of the neurons and layers is still a challenge. We propose a novel approach, named Relevance Flow, to interpret the hidden semantics of 3D point cloud classification neural networks. It delivers the class Relevance to the activated neurons in the intermediate layers in a back-propagation manner, and associates the activation of neurons with the input points to visualize the hidden semantics of each layer. Specially, we reveal that the 3D point cloud classification neural network has learned the plane-level and part-level hidden semantics in the intermediate layers, and utilize the normal and IoU to evaluate the consistency of both levels' hidden semantics. Besides, by using the hidden semantics, we generate the adversarial attack samples to attack 3D point cloud classifiers. Experiments show that our proposed method reveals the hidden semantics of the 3D point cloud classification neural network on ModelNet40 and ShapeNet, which can be used for the unsupervised point cloud part segmentation without labels and attacking the 3D point cloud classifiers.

Autonomous driving safety based on LiDAR perception is increasingly becoming a hot spot. Specifically, 3D adversarial examples always make the prediction results of deep neural network models unpredictable, which poses a major security risk to autonomous driving systems. However, the vulnerability of 3D neural network models to adversarial examples is less explored. At present, existing adversarial attack methods often obtain 3D adversarial examples by perturbing the entire point cloud, which requires a large number of perturbed points, i.e. requires a large perturbation budget. In this paper, we propose a Salient Region Adversarial attack method (SR-Adv) to generate adversarial point clouds, by perturbing fewer regions and fewer points. To our knowledge, we are the first to propose region-based attacks for 3D point clouds. First, the proposed SR-Adv employs game theory to extract salient regions of point clouds. This mechanism assigns a value to each region to measure its importance to the 3D neural network model prediction results and realizes the vulnerability analysis of the 3D model. Second, we propose a novel optimization-based gradient attack algorithm to achieve adversarial attacks on salient regions. We evaluate the proposed SR-Adv attack method on the synthetic datasets ModelNet40 and ShapeNetPart as well as the real-world dataset KITTI and NuScenes. Experimental results show that the proposed SR-Adv achieves a state-of-the-art attack success rate and better imperceptibility by perturbing fewer points on 3D point clouds.