Multimodal 3D object detection for autonomous driving, a task for real-world applications, poses substantial challenges in maintaining robust performance under various perturbations and complex environmental conditions. However, most existing approaches primarily focus on performance optimization under relatively ideal scenarios or focus on one or few disturbing conditions (or adverse conditions), lacking systematic exploration of robustness against real-world factors, including high class imbalance, adverse weather conditions, sensor jitter and failures, and significant scene variations. To address this issue, we propose a robust multimodal 3D detector, termed RobusTor3D, which integrates robustness at both the structural and supervisory levels by blending the knowledge from Vision-Language Models (VLMs). Structurally, textual descriptions are incorporated to enhance the semantic richness and diversity of rare classes. This novel semantic injection operation compensates for the inherent class imbalance and modality weakness in conventional visual features. Furthermore, semantic alignment capability and robust representation by Vision-Language Knowledge Extraction (V-LKE) serve as semantic priors to complement modality-specific representations, significantly improving model adaptability. At the supervisory level, we propose a Scene-level Multimodal Consistency Learning (SMCL) strategy, which jointly enforces global semantic constraints across modalities, encouraging the learning of stable and abundant semantic representations. This special design specifically reduces the impact of spatial alignment, while notably enabling semantic compensation under modality-loss conditions. Extensive robustness experiments conducted on KITTI, KITTI-C, and CADC benchmarks evaluate five robustness aspects, including long-tail problem, adverse weather (rain, snow, fog, strong sunlight), sensor spatial misalignment and motion blur, modality loss, and cross-domain scenarios. The results show that RobusTor3D demonstrates superior robustness across all five evaluated aspects. It consistently outperforms the state-of-the-art methods under various challenging conditions.

Collecting paired pixel-aligned hazy/haze-free image pairs in real-world is arduous for full-supervised image dehazing. Alternatively, methods employing unpaired hazy/clear images have been developed, yet their learning ability about content information of the hazy images is easily disturbed by content-independent clear images, causing artifact problems, particularly for thick hazy images. To address the above issues, we propose a new reference-based image dehazing paradigm with hazy/reference images, where the reference image is clear and taken at the same scene as the hazy image. Therefore, how to maximize the reference value from the hazy/reference images with similar content but unaligned pixels becomes a key issue. Here, we construct a reference-based contrastive learning framework to realize the effective utilization of hazy/reference image pairs. Specifically, internal contrastive learning is designed to preserve the local content invariance between the dehazed images and hazy images in a patch-wise contrastive manner, while the other external contrastive learning learns the global content consistency between the dehazed images and reference images in an overall contrastive manner. Additionally, we design a style consistency loss committee consisting of a regular adversarial loss and a style loss. The former aims to ensure each dehazed image consistent with the overall style distribution of the entire reference set, while the latter is intended to make each dehazed image have an exclusive style with the corresponding reference image. Extensive experiments corroborate that the reference-based dehazing paradigm is recommendable and reliable, and the proposed method performs admirably against other state-of-the-art methods.

The learning-based methods have recently shown their advantages in the image dehazing task. However, most existing learning-based methods do not pay much attention to the restoration in the edges of the hazy image, resulting in the edge blur of the dehazing results. To mitigate this issue, in this letter, we propose a novel Edge Aware Network (EA-Net) for image dehazing, which can simultaneously model edge features and contextual features into a single network for restoring haze-free image with sharp edges. Firstly, we extract the multi-scales contextual features of hazy image by a progressive fusion way. Furthmore, the abundant edge features are inferred by a edge subnetwork with Edge Feature Extraction Module(EFEM). Finally, we present an Edge Attention (EA) mechanism to couple the edge features with contextual features at various resolutions for sufficiently leveraging these complementary features. Due to the rich edge information and feature fusion strategy, the fused features can make the haze-free image to be clearer, especially at the edges, which is very important for the high-level vision tasks. Extensive experiments demonstrate that the proposed method achieves significant improvements over the state-of-the-art methods.