Jiang Yu Zheng
This document describes an image and video dataset of driving views captured in all weather and illumination conditions. The data set has been submitted to CDVL.
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Convolutional neural network (CNN) have proven its success for semantic segmentation, which is a core task of emerging industrial applications such as autonomous driving. However, most progress in semantic segmentation of urban scenes is reported on standard scenarios, i.e., daytime scenes with favorable illumination conditions. In practical applications, the outdoor weather and illumination are changeable, e.g., cloudy and nighttime, which results in a significant drop of semantic segmentation accuracy of CNN only trained with daytime data. In this paper, we propose a novel generative adversa…
Joint scene understanding and segmentation for automotive applications is a challenging problem in two key aspects:- (1) classifying every pixel in the entire scene and (2) performing this task under unstable weather and illumination changes (e.g. foggy weather), which results in poor outdoor scene visibility. This poor outdoor scene visibility leads to a non-optimal performance of deep convolutional neural network-based scene understanding and segmentation. In this paper, we propose an efficient end-to-end contemporary automotive semantic scene understanding approach under foggy weather condi…
We present a neural network that predicts HDR outdoor illumination from a single LDR image. At the heart of our work is a method to accurately learn HDR lighting from LDR panoramas under any weather condition. We achieve this by training another CNN (on a combination of synthetic and real images) to take as input an LDR panorama, and regress the parameters of the Lalonde-Matthews outdoor illumination model. This model is trained such that it a) reconstructs the appearance of the sky, and b) renders the appearance of objects lit by this illumination. We use this network to label a large-scale d…
AutoWeather4D: Autonomous Driving Video Weather Conversion via G-Buffer Dual-Pass Editing
本文提出AutoWeather4D框架，通过G-Buffer双步编辑分离几何与光照，实现高质量天气合成，提供细粒度物理控制，适用于自动驾驶数据生成。
A Semantic Decoupling-Based Two-Stage Rainy-Day Attack for Revealing Weather Robustness Deficiencies in Vision-Language Models
本文提出了一种基于语义解耦的两阶段雨天攻击框架，揭示了视觉-语言模型在天气鲁棒性方面的缺陷。
Clear Nights Ahead: Towards Multi-Weather Nighttime Image Restoration
针对现实中多天气与夜间光照效应共存的图像退化问题，本文构建AllWeatherNight数据集，提出ClearNight统一恢复框架，通过Retinex双重先验与天气感知协作方法，在合成及真实图像上实现SOTA性能。
IAF-Net: Illumination-Adaptive Fusion for Low-Light Urban Road Segmentation
提出IAF-Net，通过照明自适应融合模块动态调整RGB与几何特征的融合权重，并利用亮度调制注意力解码器增强低光照特征选择，实现不同光照条件下鲁棒的道路分割。
WeatherDepth: Curriculum Contrastive Learning for Self-Supervised Depth Estimation under Adverse Weather Conditions
提出WeatherDepth，通过课程对比学习和自适应调度，逐步适应恶劣天气，实现自监督深度估计的鲁棒性，在合成和真实数据集上达到最先进性能。
Adverse weather conditions are very challenging for autonomous driving because most of the state-of-the-art sensors stop working reliably under these conditions. In order to develop robust sensors and algorithms, tests with current sensors in defined weather conditions are crucial for determining the impact of bad weather for each sensor. This work describes a testing and evaluation methodology that helps to benchmark novel sensor technologies and compare them to state-of-the-art sensors. As an example, gated imaging is compared to standard imaging under foggy conditions. It is shown that gate…
WeatherBench: A Real-World Benchmark Dataset for All-in-One Adverse Weather Image Restoration
针对现有合成数据集存在域差距且缺乏真实世界数据集的问题，提出了包含多种恶劣天气和场景精确对齐图像对的基准数据集WeatherBench，为一体化图像恢复方法的训练与公平评估提供了真实世界基础。
4Seasons: A Cross-Season Dataset for Multi-Weather SLAM in Autonomous Driving
提出跨季节多天气自动驾驶SLAM数据集，含350公里九种环境记录及厘米级参考位姿，支持视觉里程计、位置识别和重定位研究。
Recent semantic segmentation models perform well under standard weather conditions and sufficient illumination but struggle with adverse weather conditions and nighttime. Collecting and annotating training data under these conditions is expensive, time-consuming, error-prone, and not always practical. Usually, synthetic data is used as a feasible data source to increase the amount of training data. However, just directly using synthetic data may actually harm the model's performance under normal weather conditions while getting only small gains in adverse situations. Therefore, we present a no…
AllWeatherNet:Unified Image Enhancement for Autonomous Driving under Adverse Weather and Lowlight-conditions
提出AllWeatherNet，一种层次化架构统一增强恶劣天气和低光图像，引入SIAM注意力机制，提升语义分割性能，mIoU提升达5.3%，并具有跨域泛化能力。
Accurate camera localization is an essential part of tracking systems. However, localization results are greatly affected by illumination. Including data collected under various lighting conditions can improve the robustness of the localization algorithm to lighting variation. However, this is very tedious and time consuming. By using synthesized images it is possible to easily accumulate a large variety of views under varying illumination and weather conditions. Despite continuously improving processing power and rendering algorithms, synthesized images do not perfectly match real images of t…
In this paper, we propose a unified end-to-end trainable multi-task network that jointly handles lane and road marking detection and recognition that is guided by a vanishing point under adverse weather conditions. We tackle rainy and low illumination conditions, which have not been extensively studied until now due to clear challenges. For example, images taken under rainy days are subject to low illumination, while wet roads cause light reflection and distort the appearance of lane and road markings. At night, color distortion occurs under limited illumination. As a result, no benchmark data…
ThermRad: A Multi-modal Dataset for Robust 3D Object Detection under Challenging Conditions
本文提出了一个包含3D LiDAR、4D雷达、RGB和热成像相机的多模态数据集ThermRad，并设计了RTDF-RCNN融合方法，验证了4D雷达与热成像相机在恶劣天气下进行鲁棒3D目标检测的有效性。
Camera localization is a fundamental and crucial problem for many robotic applications. In recent years, using deep-learning for camera-based localization has become a popular research direction. However, they lack robustness to large domain shifts, which can be caused by seasonal or illumination changes between training and testing data sets. Data augmentation is an attractive approach to tackle this problem, as it does not require additional data to be provided. However, existing augmentation methods blindly perturb all pixels and therefore cannot achieve satisfactory performance. To overcom…
Object detection in road scenes is necessary to develop both autonomous vehicles and driving assistance systems. Even if deep neural networks for recognition task have shown great performances using conventional images, they fail to detect objects in road scenes in complex acquisition situations. In contrast, polarization images, characterizing the light wave, can robustly describe important physical properties of the object even under poor illumination or strong reflections. This paper shows how non-conventional polarimetric imaging modality overcomes the classical methods for object detectio…
Outdoor webcam images are an information-dense yet accessible visualization of past and present weather conditions, and are consulted by meteorologists and the general public alike. Weather forecasts, however, are still communicated as text, pictograms or charts. We therefore introduce a novel method that uses photographic images to also visualize future weather conditions. This is challenging, because photographic visualizations of weather forecasts should look real, be free of obvious artifacts, and should match the predicted weather conditions. The transition from observation to forecast sh…
This study developed a traffic sign detection and recognition algorithm based on the RetinaNet. Two main aspects were revised to improve the detection of traffic signs: image cropping to address the issue of large image and small traffic signs; and using more anchors with various scales to detect traffic signs with different sizes and shapes. The proposed algorithm was trained and tested in a series of autonomous driving front-view images in a virtual simulation environment. Results show that the algorithm performed extremely well under good illumination and weather conditions. Its drawbacks a…
Nordic Vehicle Dataset (NVD): Performance of vehicle detectors using newly captured NVD from UAV in different snowy weather conditions
针对大雪天无人机车辆检测数据缺失的研究空白，构建了覆盖北欧多种雪天条件的Nordic Vehicle Dataset，评估了主流目标检测模型性能并提出有效数据增强方案，将公开数据与代码。
PairedGTA: Generating Driving Datasets for Controlled Photometric Shift Analysis
提出基于高保真游戏引擎的PairedGTA框架，通过生成完美配对的图像，实现独立于几何和语义变化的光度偏移分析，并用于评估语义分割模型在恶劣条件下的性能退化。
All-weather Multi-Modality Image Fusion: Unified Framework and 100k Benchmark
本文提出统一的全天候多模态图像融合框架，通过联合特征融合与恢复提升关键场景信息表征，构建10万张图像对的多模态数据集，实验证明在真实和合成场景中图像融合及下游任务表现优异。
Benchmarking Nighttime Traffic Sign Recognition with Illumination-Adaptive Detection and Semantic Attribute Reasoning
本文提出INTSD数据集和LENS-Net算法，解决夜间交通标志识别中的低光照和干扰问题，通过大规模数据和先进模型验证了夜间识别的挑战与方法。
RadarMP: Motion Perception for 4D mmWave Radar in Autonomous Driving
针对4D毫米波雷达因点云稀疏噪声导致运动感知不精确的问题，提出RadarMP方法，通过统一架构联合建模目标检测与运动估计，利用自监督损失函数提升感知性能，增强自动驾驶系统在恶劣条件下的感知能力。
We describe the release of reference data towards a challenging long-term localisation and mapping benchmark based on the large-scale Oxford RobotCar Dataset. The release includes 72 traversals of a route through Oxford, UK, gathered in all illumination, weather and traffic conditions, and is representative of the conditions an autonomous vehicle would be expected to operate reliably in. Using post-processed raw GPS, IMU, and static GNSS base station recordings, we have produced a globally-consistent centimetre-accurate ground truth for the entire year-long duration of the dataset. Coupled wit…
Exploring Domain Shift on Radar-Based 3D Object Detection Amidst Diverse Environmental Conditions
本研究首次综合分析了4D雷达三维目标检测中的域偏移问题，揭示了天气、道路类型变化引发的显著域偏移及雷达点云生成的关键作用，为环境变化下的雷达数据采集策略提供参考。
Towards Physically-Based Sky-Modeling For Image Based Lighting
本文提出AllSky模型，通过直接学习物理捕获的HDRI数据，实现更逼真、支持全动态范围的天空建模，突破现有深度学习方法的局限。
DSERT-RoLL: Robust Multi-Modal Perception for Diverse Driving Conditions with Stereo Event-RGB-Thermal Cameras, 4D Radar, and Dual-LiDAR
本文提出DSERT-RoLL数据集，结合立体事件、RGB和热成像相机以及4D雷达和双光雷达，涵盖多样的天气和光照条件，提供精确的2D和3D边界框及轨迹ID，支持跨传感器组合的公平比较，提升新型传感器的数据可用性，并建立统一的3D和2D基准，促进传感器性能的系统研究。
Learning from Rendering: Realistic and Controllable Extreme Rainy Image Synthesis for Autonomous Driving Simulation
针对现有降雨图像合成器光照可控性差、真实感不足的问题，本研究提出结合渲染与学习优势的合成方法，集成CARLA构建CARLARain仿真器，可生成标注配对的极端降雨图像，有效提升语义分割模型在极端雨景下的性能。
Audio-Visual Dataset and Method for Anomaly Detection in Traffic Videos
针对交通异常检测，研究提出首个真实场景视听数据集MAVAD，并设计了通过交叉注意力融合视听特征的AVACA方法，证实音频提升性能达5.2%且图像匿名化影响轻微。
Perception Characteristics Distance: Measuring Stability and Robustness of Perception System in Dynamic Conditions under a Certain Decision Rule
提出感知特性距离（PCD）作为衡量动态条件下感知系统稳定性与鲁棒性的新指标，并通过SensorRainFall数据集验证其有效性。
Visual localization is a crucial component in the application of mobile robot and autonomous driving. Image retrieval is an efficient and effective technique in image-based localization methods. Due to the drastic variability of environmental conditions, e.g. illumination, seasonal and weather changes, retrieval-based visual localization is severely affected and becomes a challenging problem. In this work, a general architecture is first formulated probabilistically to extract domain invariant feature through multi-domain image translation. And then a novel gradient-weighted similarity activat…
Failure or Drift? Evaluating Monocular SLAM under Synthetic and Real-World Corruptions
本研究评估单目SLAM在合成与真实世界干扰下的表现，区分跟踪失效与漂移，发现学习型跟踪器会产生持续漂移，且其排序随干扰物理保真度变化。
Accurate and robust visual localization under a wide range of viewing condition variations including season and illumination changes, as well as weather and day-night variations, is the key component for many computer vision and robotics applications. Under these conditions, most traditional methods would fail to locate the camera. In this paper we present a visual localization algorithm that combines structure-based method and image-based method with semantic information. Given semantic information about the query and database images, the retrieved images are scored according to the semantic …
Bridging Clear and Adverse Driving Conditions
针对自动驾驶系统在恶劣条件下性能下降、相关数据集不足的问题，提出新型域适应流水线，通过多种数据生成方法合成逼真恶劣条件图像，结合模拟与真实图像优化训练，在ACDC数据集上提升了语义分割性能。
One of the key problems in computer vision is adaptation: models are too rigid to follow the variability of the inputs. The canonical computation that explains adaptation in sensory neuroscience is divisive normalization, and it has appealing effects on image manifolds. In this work we show that including divisive normalization in current deep networks makes them more invariant to non-informative changes in the images. In particular, we focus on U-Net architectures for image segmentation. Experiments show that the inclusion of divisive normalization in the U-Net architecture leads to better se…
DriveIndia: An Object Detection Dataset for Diverse Indian Traffic Scenes
该研究构建DriveIndia大规模目标检测数据集，覆盖印度多样交通场景与条件，含66986张图像及24类目标，提供YOLO模型基线（最佳mAP50为78.7%），助力不确定路况下鲁棒检测研究并将公开。
Single-view place recognition, that we can define as finding an image that corresponds to the same place as a given query image, is a key capability for autonomous navigation and mapping. Although there has been a considerable amount of research in the topic, the high degree of image variability (with viewpoint, illumination or occlusions for example) makes it a research challenge. One of the particular challenges, that we address in this work, is weather variation. Seasonal changes can produce drastic appearance changes, that classic low-level features do not model properly. Our contributions…
SatDepth: A Novel Dataset for Satellite Image Matching
SatDepth数据集通过引入图像旋转增强技术，提升卫星图像匹配的精度，验证了其在大旋转差异下的优越性能
Recognition of defects in concrete infrastructure, especially in bridges, is a costly and time consuming crucial first step in the assessment of the structural integrity. Large variation in appearance of the concrete material, changing illumination and weather conditions, a variety of possible surface markings as well as the possibility for different types of defects to overlap, make it a challenging real-world task. In this work we introduce the novel COncrete DEfect BRidge IMage dataset (CODEBRIM) for multi-target classification of five commonly appearing concrete defects. We investigate and…
Freespace detection is an essential component of visual perception for self-driving cars. The recent efforts made in data-fusion convolutional neural networks (CNNs) have significantly improved semantic driving scene segmentation. Freespace can be hypothesized as a ground plane, on which the points have similar surface normals. Hence, in this paper, we first introduce a novel module, named surface normal estimator (SNE), which can infer surface normal information from dense depth/disparity images with high accuracy and efficiency. Furthermore, we propose a data-fusion CNN architecture, referre…
Domain-Incremental Semantic Segmentation for Autonomous Driving under Adverse Driving Conditions
针对恶劣天气与光照下语义分割性能下降及持续学习中的灾难性遗忘问题，提出动态增长的PSS架构，并用卷积自编码器识别领域、选择专用分割模块，验证了其多数据集表现与跨领域泛化能力。
Visual localization is the problem of estimating the position and orientation from which a given image (or a sequence of images) is taken in a known scene. It is an important part of a wide range of computer vision and robotics applications, from self-driving cars to augmented/virtual reality systems. Visual localization techniques should work reliably and robustly under a wide range of conditions, including seasonal, weather, illumination and man-made changes. Recent benchmarking efforts model this by providing images under different conditions, and the community has made rapid progress on th…
Quaternion Infrared Visible Image Fusion
针对现有红外-可见光融合忽略颜色结构且在低质量输入下性能下降的问题，提出完全在四元数域处理的QIVIF框架，通过低能见度特征学习、自适应反锐化掩模和分层贝叶斯融合实现高质量融合。
A paired synthetic construction-site image dataset for robust computer vision under adverse conditions
针对施工图像数据集缺乏恶劣条件样本的问题，提出成对合成数据集ConSynth-X，含34,199张图像，覆盖多种恶劣条件，支持多任务并验证了其保真度，以提升视觉模型鲁棒性。
Moonworks Lunara Aesthetic II: An Image Variation Dataset
Lunara Aesthetic II 是一个用于评估图像生成和编辑系统中上下文一致性的公开数据集，通过保持身份的上下文变换提供监督信号，具有高美学评分和稳定的身份特征。
ROVR-Open-Dataset: A Large-Scale Depth Dataset for Autonomous Driving
本文提出ROVR-Open-Dataset，一个大规模、多样化且成本效益高的深度数据集，用于提升自动驾驶中空间感知的能力，通过提供丰富的场景、光照和天气条件数据，以及经过验证的地面真实数据，支持鲁棒的模型训练，并识别出当前架构共享的三种失败模式。
In this work, we propose a novel crowd counting network that progressively generates crowd density maps via residual error estimation. The proposed method uses VGG16 as the backbone network and employs density map generated by the final layer as a coarse prediction to refine and generate finer density maps in a progressive fashion using residual learning. Additionally, the residual learning is guided by an uncertainty-based confidence weighting mechanism that permits the flow of only high-confidence residuals in the refinement path. The proposed Confidence Guided Deep Residual Counting Network…
Fusion Meets Diverse Conditions: A High-diversity Benchmark and Baseline for UAV-based Multimodal Object Detection with Condition Cues
针对无人机多模态检测数据集条件多样性不足的问题，提出高多样性基准ATR-UMOD及条件感知动态融合方法PCDF，利用条件提示自适应融合RGB与红外信息，实验验证其有效性。
Understanding animals' behaviors is significant for a wide range of applications. However, existing animal behavior datasets have limitations in multiple aspects, including limited numbers of animal classes, data samples and provided tasks, and also limited variations in environmental conditions and viewpoints. To address these limitations, we create a large and diverse dataset, Animal Kingdom, that provides multiple annotated tasks to enable a more thorough understanding of natural animal behaviors. The wild animal footages used in our dataset record different times of the day in extensive ra…
One of the fundamental challenges in the design of perception systems for autonomous vehicles is validating the performance of each algorithm under a comprehensive variety of operating conditions. In the case of vision-based semantic segmentation, there are known issues when encountering new scenarios that are sufficiently different to the training data. In addition, even small variations in environmental conditions such as illumination and precipitation can affect the classification performance of the segmentation model. Given the reliance on visual information, these effects often translate …
MUSES: The Multi-Sensor Semantic Perception Dataset for Driving under Uncertainty
MUSES是一个多传感器语义感知数据集，包含2500张不同天气和光照下的图像，集成了多种传感器，提出不确定性感知全景分割任务，用于评估和训练自动驾驶感知模型。
We present a method for localizing a single camera with respect to a point cloud map in indoor and outdoor scenes. The problem is challenging because correspondences of local invariant features are inconsistent across the domains between image and 3D. The problem is even more challenging as the method must handle various environmental conditions such as illumination, weather, and seasonal changes. Our method can match equirectangular images to the 3D range projections by extracting cross-domain symmetric place descriptors. Our key insight is to retain condition-invariant 3D geometry features f…
For human drivers, having rear and side-view mirrors is vital for safe driving. They deliver a more complete view of what is happening around the car. Human drivers also heavily exploit their mental map for navigation. Nonetheless, several methods have been published that learn driving models with only a front-facing camera and without a route planner. This lack of information renders the self-driving task quite intractable. We investigate the problem in a more realistic setting, which consists of a surround-view camera system with eight cameras, a route planner, and a CAN bus reader. In parti…
Geometry-guided Cross-view Diffusion for One-to-many Cross-view Image Synthesis
针对跨视角图像合成的一对多不确定性问题，本文提出几何引导跨视角扩散方法，引入GCC策略建立特征几何对应，在三个基准数据集上证明其生成图像的质量、保真度和多样性优于现有方法。
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