Open-set driving in complex real-world environments requires reliable identification of out-of-distribution (OOD) objects to avoid overconfident predictions on unseen categories. However, the sparsity and limited semantic richness of LiDAR point clouds make separating known and unknown classes difficult. This work proposes DOODLE, a diffusion model–based OOD learning framework for open-set 3D semantic segmentation. DOODLE trains a diffusion model to reconstruct in-distribution semantic features; feature-level reconstruction discrepancies then serve as OOD evidence. The resulting OOD scores are used to enhance backbone semantic features, improving discrimination of unknown regions during segmentation. To further reduce false positives arising from nonuniform measurements, a density-aware post-processing (DAP) module incorporates spatial variation in LiDAR point density when refining OOD predictions. DOODLE integrates seamlessly with existing open-set models and does not constrain backbone design. Experiments on SemanticKITTI and nuScenes demonstrate state-of-the-art OOD performance. On SemanticKITTI, DOODLE improves area under the precision–recall curve (AUPR) by 1.85%p and area under the receiver operating characteristic (AUROC) by 1.29%p over prior methods. Ablation studies confirm complementary benefits from diffusion-based reconstruction and DAP. Code is available at https://github.com/chang9711/DOODLE.

Deep neural networks are known to be vulnerable to security risks due to the inherent transferable nature of adversarial examples. Despite the success of recent generative model-based attacks demonstrating strong transferability, it still remains a challenge to design an efficient attack strategy in a real-world strict black-box setting, where both the target domain and model architectures are unknown. In this paper, we seek to explore a feature contrastive approach in the frequency domain to generate adversarial examples that are robust in both cross-domain and cross-model settings. With that goal in mind, we propose two modules that are only employed during the training phase: a Frequency-Aware Domain Randomization (FADR) module to randomize domain-variant low- and high-range frequency components and a Frequency-Augmented Contrastive Learning (FACL) module to effectively separate domain-invariant mid-frequency features of clean and perturbed image. We demonstrate strong transferability of our generated adversarial perturbations through extensive cross-domain and cross-model experiments, while keeping the inference time complexity.

Semantic segmentation and depth estimation lie at the heart of scene understanding and play crucial roles especially for autonomous driving. In particular, it is desirable for an intelligent self-driving agent to discern unexpected obstacles on the road ahead reliably in real-time. While existing semantic segmentation studies for small road hazard detection have incorporated fusion of multiple modalities, they require additional sensor inputs and are often limited by a heavyweight network for real-time processing. In this light, we propose an end-to-end Real-time Obstacle Detection via Simultaneous refinement, coined RODSNet (https://github.com/SAMMiCA/RODSNet) which jointly learns semantic segmentation and disparity maps from a stereo RGB pair and refines them simultaneously in a single module. RODSNet exploits two efficient single-task network architectures and a simple refinement module in a multi-task learning scheme to recognize unexpected small obstacles on the road. We validate our method by fusingCityscapesandLost and Founddatasets and show that our method outperforms previous approaches on the obstacle detection task, even recognizing the unannotated obstacles at 14.5 FPS on our fused dataset ($2048\times 1024$resolution) using RODSNet-$2\times $. In addition, extensive ablation studies demonstrate that our simultaneous refinement effectively facilitates contextual learning between semantic and depth information.