kitti semantic segmentation

1. Example of 2D semantic segmentation: (Top) input image ... Why Vimeo? Real-time Semantic Scene Completion Christopher Agia, Ran Cheng Paper in preparation, 2020. In addition, the dataset provides different variants of these sequences such as modified weather conditions (e.g. In this paper we are interested in exploiting geographic priors to help outdoor scene understanding. The KITTI Vision Benchmark Suite - Cvlibs Panoptic segmentation is the recently introduced task that tackles semantic segmentation and instance segmentation jointly [18]. Our labeling tool provides the following features and capabilities: Different tools to annotate the point cloud data, including polygon-based or brush-based labeling and filtering. Note here that this is significantly different from classification. Each frame is processed indiv. fog, rain) or modified camera configurations (e.g. The dataset is directly derived from the Virtual KITTI Dataset (v.1.3.1). A Benchmark for LiDAR-based Panoptic Segmentation based on ... KITTI Semantic Segmentation Benchmark (Semantic ... Dataset Overview - Cityscapes Dataset - Semantic ... Example of PointCloud semantic segmentation. Panoptic segmentation is the recently introduced task that tackles semantic segmentation and instance segmentation jointly [18]. Note that only the published methods are considered. An understanding of open data sets for urban semantic segmentation shall help one understand how to proceed while training models for self-driving cars. We use ResNet-101 or ResNet-152 networks that have been pretrained on the ImageNet dataset as a starting point for all of our models. In this paper, we propose a more efficient neural network architecture, which has fewer parameters, for semantic . Monocular Bird's-Eye-View Semantic Segmentation for ... For example, [ 14 ] shows how to jointly classify pixels and predict their depth using a multi-class decision stumps-based boosted classifier. We anno-tated all sequences of the KITTI Vision Odometry Bench-markandprovidedensepoint-wiseannotationsforthecom-plete360o field-of-view ofthe employedautomotiveLiDAR. Semantic Segmentation via Multi-task, Multi-domain ... GitHub - navganti/kitti_scripts: Github hosting of the ... 50 cities; Several months (spring, summer, fall) Abstract—Panoptic segmentation is the recently introduced task that tackles semantic segmentation and instance segmen-tation jointly [18]. Most state-of-the-art methods focus on accuracy, rather than efficiency. We designed baseline softmax regression and maximum likelihood estimation, which performs quite Semantic Segmentation Datasets for Urban Driving Scenes ... Getting Started with FCN Pre-trained Models. In this paper, we present ViP-DeepLab, a unified model attempting to tackle the long-standing and challenging inverse projection problem in vision, which we model as restoring the point clouds from perspective image sequences while providing each point with instance-level semantic interpretations. Multiple image segmentation algorithms have been developed. Figure 4(b) shows the normalized reprojection errors and keypoint counts about 12 semantic segmentation categories, such as Sky , Building , Pole , Road Marking , Road , Pavement , Tree , Sign Symbol , Fence , Vehicle , Pedestrian , and Bike . Other independent groups have annotated. We propose three benchmark tasks based on this dataset: (i) semantic segmentation of point clouds using a single We applied sparse convolution and transpose convolution on raw Kitti Velodyne point cloud data to predict dense semantic segmentation of BEV masks. Specifically, the encoder adopts a novel squeeze nonbottleneck module as a base . Semantic Segmentation with Pytorch-Lightning. In a typical autonomous driving stack, Behavior Prediction and Planning are generally done in this a top-down view (or bird's-eye-view, BEV), as hight information is less important and most of the information an autonomous vehicle would need can be conveniently represented . 2. In this paper, we present an extension of SemanticKITTI [1], a large-scale dataset providing dense point-wise semantic labels for all sequences of the KITTI Odometry Benchmark [10]. It consists of 200 semantically annotated train as well as 200 test images corresponding to the KITTI Stereo and Flow Benchmark 2015. KITTI image segmentation sample - Source: KITTI Image Segmentation and Deep Learning. Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges Di Feng*, Christian Haase-Schuetz*, Lars Rosenbaum, Heinz Hertlein, Claudius Glaeser, Fabian Timm, Werner Wiesbeck and Klaus Dietmayer Intro Semantic segmentation is no more than pixel-level classification and is well-known in the deep-learning community. Large-scale SemanticKITTI is based on the KITTI Vision Benchmark and we provide semantic annotation for all sequences of the Odometry Benchmark. I removed the dropout layer from the original FCN and added batchnorm to the encoder. It is derived from the KITTI Vision Odometry Benchmark which it extends with dense point-wise annotations for the complete 360 field-of-view of the employed automotive LiDAR. Semantic Segmentation I have implemented semantic segmentation using Kitti Road dataset dataset. ICCV'W17) Exploring Spatial Context for 3D Semantic Segmentation of Point Clouds paper. Accurate and efficient segmen-tation mechanisms are required. For qualitative evaluation, Figure 3 and Figure 4 show some semantic-segmentation results generated by the 3D point-cloud segmentation network on the Semantic-KITTI test set. IROS'2019 submission - Andres Milioto, Ignacio Vizzo, Jens Behley, Cyrill Stachniss.Predictions from Sequence 13 Kitti dataset. The image names are prefixed by the dataset's benchmark name. Communicate internally. KITTI, SUN-RGBD : Dou et al., 2019 LiDAR, visual camera: 3D Car: LiDAR voxel (processed by VoxelNet), RGB image (processed by a FCN to get semantic features) Two stage detector: Predictions with fused features: Before RP: Feature concatenation: Middle: KITTI : Sindagi et al., 2019 LiDAR, visual camera: 3D Car datasets (Camvid, KITTI, U-LabelMe, CBCL) for the task of semantic segmentation based on DCNNs, i.e. In this paper, we introduce a large dataset to propel research on laser-based semantic segmentation. Semantic segmentation is the task of assigning a class to every pixel in a given image. Human-readable label description files in xml allow to define label names, ids, and colors. the KITTI semantic segmentation test set, which surpasses the winning entry of the ROB challenge 2018. Specifically, a multiscale fusion module is proposed to extract effective features from data of different modalities, and a channel attention module is used to . Semantic Segmentation¶file_downloadDownload all examples in Python source code: examples_segmentation_python.zipfile_downloadDownload all examples in Jupyter notebooks: examples_segmentation_jupyter.zip. Many applications, such as autonomous driving and robot navigation with urban road scene, need accurate and efficient segmentation. Download scientific diagram | Semantic segmentation ablation experiment on Virtual KITTI dataset. An important tasks in semantic scene understanding is the task of semantic segmentation. Towards this goal we propose a holistic approach that reasons jointly about 3D object detection, pose estimation, semantic segmentation as well as depth reconstruction from a single . file_download. Virtual KITTI 3D Dataset for Semantic Segmentation This is the outdoor dataset used to evaluate 3D semantic segmentation of point clouds in ( Engelmann et al. It consists of hours of traffic scenarios recorded with a variety of sensor modalities, including high-resolution RGB, grayscale stereo cameras, and a 3D laser scanner. Panoptic segmentation is the recently introduced task that tackles semantic segmentation and instance segmentation jointly. We demonstrate our results in the KITTI benchmark and the Semantic3D benchmark. the use of the combined data significantly boosts the performanceob-tained when using the real-world data alone. The results show that Adapnet++ performs better on RGB images than depth images, which is consistent with the results of the original Adapnet++ study on real images. See a full comparison of 5 papers with code. Overview. semantic segmentation via a data-fusion CNN architecture, which greatly en-hanced the performance of driving scene segmentation. The Kitti 2015 segmentation format (TODO) is used as common format for all datasets. The difference in input to BEV semantic segmentation vs SLAM (Image by the author of this post)Why BEV semantic maps? If done correctly, one can delineate the contours of all the objects appearing on the input image. We propose three benchmark tasks based on this dataset: (i . Setup Frameworks and Packages Make sure you have the following is installed: Python 3 TensorFlow NumPy SciPy Dataset Download the Kitti Road dataset from here. This paper introduces an updated version of the well-known Virtual KITTI dataset which consists of 5 sequence clones from the KITTI tracking benchmark. Setup Make sure you have the following is installed: python 3.5 tensorflow 1.2.1 Etc. For example if there are 2 cats in an image, semantic segmentation gives same label to all the pixels of both cats The current state-of-the-art on KITTI Semantic Segmentation is DeepLabV3Plus + SDCNetAug. We have evaluated In-tersection over Union (IoU) metric over Cityscapes and KITTI datasets. Examples of Kitti data set sequence (a) 00, (b) 01, and (c) 02 and their semantic segmentation results. The benchmark requires to assign segmentation and tracking labels to all pixels. Market your business. Panoptic segmentation is the recently introduced task that tackles semantic segmentation and instance segmentation jointly [18]. The KITTI Vision Benchmark Suite Semantic Instance Segmentation Evaluation This is the KITTI semantic instance segmentation benchmark. file_download. The visualization results including a point cloud, an image, predicted 3D bounding boxes and their projection on the image will be saved in $ {OUT_DIR}/PCD_NAME. KITTI: The KITTI vision benchmark suite (Geiger et al 2013) is one of the most comprehensive datasets that provides groundtruth for a variety of tasks such as semantic segmentation, scene flow estimation, optical flow estimation, depth prediction, odometry estimation, tracking and road lane detection. This is a simple demo for performing semantic segmentation on the Kitti dataset using Pytorch-Lightning and optimizing the neural network by monitoring and comparing runs with Weights & Biases.. Pytorch-Ligthning includes a logger for W&B that can be called simply with:from pytorch_lightning.loggers import . Left, input dense point cloud with RGB information. Currently. The future work that we foresee given these results is pointed out in section 6, together with the conclusions of the paper. It is derived from the KITTI Vision Odometry Benchmark which it extends with dense point-wise annotations for the complete 360 field-of-view of the employed automotive LiDAR. which simultaneously performs semantic segmentation and depth estimation. Panoptic segmentation is the recently introduced task that tackles semantic segmentation and instance segmentation jointly. Please, use the following link to access our demo project. In this work, we introduce a new neural network to perform semantic segmentation of a full 3D LiDAR point cloud in real-time. [11], [12] also propose a multi-modal sensor-based semantic 3D mapping system to improve the segmentation results in terms of the intersection-over-union (IoU) metric, in large-scale . The total KITTI dataset is not only for semantic segmentation, it also includes dataset of 2D and 3D object detection, object tracking, road/lane detection, scene flow, depth evaluation, optical flow and semantic instance level segmentation. The data can be downloaded here: Download label for semantic and instance segmentation (314 MB) KITTI-360 KITTI-360: A large-scale dataset with 3D&2D annotations About We present a large-scale dataset that contains rich sensory information and full annotations. In recent years, convolutional neural networks (CNNs) have been at the centre of the advances and progress of advanced driver assistance systems and autonomous driving. Semantic Segmentation Editor: Point cloud labeling overview on Vimeo. For object detection/recognition, instead of just putting rectangular boxes . In this paper, we present an extension of SemanticKITTI [1], a large-scale dataset providing dense point-wise semantic labels for all sequences of the KITTI Odometry Benchmark [10]. Show multiple scans, but also single scans for every time step. search on laser-based semantic segmentation. Jeong et al. Overall, we provide an unprecedented number of scans covering the full 360 degree field-of-view of the employed automotive LiDAR. This paper presents a point-wise pyramid attention network, namely, PPANet, which employs an encoder-decoder approach for semantic segmentation. In order to better understand the model output, we perform an analysis on the common prototypes and coefficients learned for both motion and semantic instance segmentation. There are several "state of the art" approaches for building such models. Solving this problem requires the vision models to predict the spatial location, semantic class . Monetize your videos. In order to compare the results more easily, Figure 3 shows the results using spherical projection, with each color representing a different semantic class. However, it still has not expanded its . The KITTI semantic segmentation dataset consists of 200 semantically annotated training images and of 200 test images. Extract the dataset in the data folder. inferring semantic labels on KITTI, while still being able to segment unknown moving objects that exist in DAVIS dataset. The results show that Adapnet++ performs better on RGB images than depth images, which is consistent with the results of the original Adapnet++ study on real images. Figure 1. Classification assigns a single class to the whole image whereas semantic segmentation classifies every pixel of the image to one of the classes. Zhou et al. Multiclass semantic segmentation on cityscapes and kitti datasets. 2 . Our labeling tool provides the following features and capabilities: Different tools to annotate the point cloud data, including polygon-based or brush-based labeling and filtering. Test with PSPNet Pre-trained Models. mentation networks for the semantic segmentation part. We report our experiments and results on three challenging semantic segmentation datasets: Cityscapes [10], KITTI dataset [15] for road estimation, and PASCAL VOC2012 [13]. KITTI KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) is one of the most popular datasets for use in mobile robotics and autonomous driving. 1. Semantic segmentation is a significant technique that can provide valuable insights into the context of driving scenes. Dense semantic segmentation; Instance segmentation for vehicle and people; Complexity. I used the FCN architecture. To test a 3D detector on multi-modality data (typically point cloud and image), simply run: where the ANNOTATION_FILE should provide the 3D to 2D projection matrix. This extension enables training and evaluation of LiDAR-based panoptic segmentation . Sequential The dataset consists of 22 sequences. The KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) dataset was released in 2012, but not with semantically segmented images. rotated by 15 degrees). The KITTI dataset [], another autonomous driving dataset recorded by driving on highways and in rural areas around Karlsruhe, is another example of semantic image data.On average, a maximum of 15 cars and 30 pedestrians can be seen in each image. In this paper, we present an extension of SemanticKITTI, which is a large-scale dataset providing dense point-wise semantic labels for all sequences of the KITTI Odometry Benchmark, for training and evaluation of laser-based panoptic segmentation. We annotated all sequences of the KITTI Vision Odometry Benchmark and provide dense point-wise annotations for the complete $360^{o}$ field-of-view of the employed automotive LiDAR. The contributions of the proposed method can be listed as: A deep neural network which can be trained end-to-end to estimate the semantic grids by . Exactly the same image names are used for the input images and the ground truth files. ; Diversity. The semantic segmentation prediction follows the typical design of any semantic segmentation model (e.g., DeepLab), while the instance segmentation prediction involves a simple instance center regression, where the model learns to predict instance centers as well as the offset from each pixel to its corresponding center. 30 classes; See Class Definitions for a list of all classes and have a look at the applied labeling policy. network with supervision on both semantic segmentation anddisparityestimation. For the 7 subsets of the KITTI dataset used in this paper [9, 13, 14, 18, 19, 22, 25], deep learning has never been used to tackle the semantic segmentation step. Lightning Kitti. The data format and metrics are conform with The Cityscapes Dataset. SemanticKITTI is a large-scale outdoor-scene dataset for point cloud semantic segmentation. Explore semantic segmentation datasets like Mapillary Vistas, Cityscapes, CamVid, KITTI and DUS. Semantic segmentation: Virtual KITTI 2's ground-truth semantic segmentation annotations were used to evaluate the state-of-the-art urban scene segmentation method, Adapnet++ [9]. See Figure 1 for an example of semantic segmentation of PointClouds in the Semantic3D dataset. KITTI. For each sequence, we provide multiple sets of images . Semantic segmentation is a computer vision task of assigning each pixel of a given image to one of the predefined class labels, e.g., road, pedestrian, vehicle, etc. This article is a comprehensive overview including a step-by-step guide to implement a deep learning image segmentation model.. We shared a new updated blog on Semantic Segmentation here: A 2021 guide to Semantic Segmentation Nowadays, semantic segmentation is one of the key problems in the field of computer vision. In this paper, we introduce a large dataset to propel research on laser-based semantic segmentation. Multi-modality demo. First, the per-pixel semantic segmentation of over 700 images was specified manually, and was then inspected and confirmed by a second person for accuracy. Polygonal annotations. In this paper, we introduce a large dataset to propel research on laser-based semantic segmentation. Instance segmentation extends the scope of semantic segmentation further by detecting and delineating all the objects of interest in an image. Looking at the big picture, semantic segmentation is one of the high-level . SemanticKITTI SemanticKITTI is a large-scale outdoor-scene dataset for point cloud semantic segmentation. This work discusses several mechanisms: data augmentation, transfer learning, transposed convolutions and focal loss function for improving the performance of neural networks for image segmentation. We recorded several suburbs of Karlsruhe, Germany, corresponding to over 320k images and 100k laser scans in a driving distance of 73.7km. proposed a model for evaluating the clarity of screen content and natural scene images while blind []. The use of multimodal sensors for lane line segmentation has become a growing trend. KITTI semantic segmentation dataset [5] State of the Art Research in the field of image segmentation: These state of the art methods are known hugely in the field of image segmentation. Image semantic segmentation is of immense interest for self-driving car research. Collaborate on video. best on the KITTI road benchmark3 [15]. In this paper, we explicitly address semantic segmentation for rotating 3D LiDARs such as In this paper, we present an extension of SemanticKITTI, which is a large-scale dataset providing dense point-wise semantic labels for all sequences of the KITTI Odometry Benchmark, for training and evaluation of laser . The remainder of this paper is structured as follows: Section2provides an Semantic segmentation:- Semantic segmentation is the process of classifying each pixel belonging to a particular label. Weconductcomprehensiveexper-iments, including a series of ablation studies and compari-son tests of SSPCV-Net with existing state-of-the-art meth-ods on Scene Flow, KITTI 2015 and KITTI 2012 bench-mark datasets, and moreover, we also perform tests on In this paper, we present an extension of SemanticKITTI [1], a large-scale dataset providing dense point-wise semantic labels for all sequences of the KITTI Odometry Benchmark [10]. The data format and metrics are conform with The Cityscapes Dataset. To combine RGB image and dense depth map more effectively for instance segmentation, inspired by recent multi-modal fusion models [12, 19], a sharpening mixture of experts (SMoE) fusion network is proposed based on the real-time instance segmentation network YOLACT [] to automatically learn the contribution of each modality for instance segmentation in complex scenes. We annotated all sequences of the KITTI Vision Odometry Benchmark and provide dense point-wise annotations for the complete $360^{o}$ field-of-view of the employed automotive LiDAR. Semantic segmentation assigns a class label to each data point in the input modality, i.e., to a pixel in case of a camera or to a 3D point obtained by a LiDAR. It consists of 200 semantically annotated train as well as 200 test images corresponding to the KITTI Stereo and Flow Benchmark 2015. This is the KITTI semantic segmentation benchmark. Note that only the published methods are considered. The goal of this task is to encourage . MOPT unifies the distinct tasks of semantic segmentation (pixel-wise classification of 'stuff' and 'thing' classes), instance segmentation (detection and segmentation of instance-specific 'thing' classes) and multi-object tracking (detection and association of 'thing' classes over time). from publication: Simultaneous Semantic Segmentation and Depth Completion with Constraint of . Earlier methods include thresholding, histogram-based . To achieve robust multimodal fusion, we introduced a new multimodal fusion method and proved its effectiveness in an improved fusion network. In this paper, we rst introduce a novel module named surface normal es- . Second, the high-quality and large resolution color video images in the database represent valuable extended duration digitized footage to those interested in driving scenarios or ego-motion. Holistic 3D Scene Understanding from a Single Geo-tagged Image. Introduction Semantic segmentation is the task of dense per pixel pre-dictions of semantic labels. Meanwhile, adversarial training is ap-plied on the joint output space to preserve the correlation between semantics and depth. Semantic Segmentation Introduction In this project, you'll label the pixels of a road in images using a Fully Convolutional Network (FCN). We annotated all sequences of the KITTI Vision Odometry Benchmark and provide dense point-wise annotations for the complete 360-degree field-of-view of the employed automotive LiDAR. It doesn't different across different instances of the same object. Semantic segmentation: Virtual KITTI 2's ground-truth semantic segmentation annotations were used to evaluate the state-of-the-art urban scene segmentation method, Adapnet++ [9]. In this paper, we present an extension of SemanticKITTI [1], a large-scale dataset providing dense point-wise semantic labels for all sequences of the KITTI Odometry Benchmark [10]. In exploiting geographic priors to help outdoor scene understanding convolution and transpose convolution on KITTI. Our demo project prefixed by the dataset is directly derived from the original FCN and added batchnorm to the image... > [ 2012.05258 ] ViP-DeepLab: Learning Visual Perception with... < /a > Multi-modality demo //www.open3d.org/2019/01/16/on-point-clouds-semantic-segmentation/. Autonomous driving and robot navigation with urban road scene, need accurate and efficient segmentation Figure 1 for an of. Of BEV masks starting point for all of our models classification assigns a kitti semantic segmentation class to the KITTI benchmark3... Best on the KITTI semantic segmentation dataset consists of 200 semantically annotated train as well as test! 200 semantically annotated training images and 100k laser scans in a driving distance of.... But kitti semantic segmentation single scans for every time step iccv & # x27 ; W17 ) Spatial! Of 200 test images large-scale SemanticKITTI is based on the joint output space preserve! Same object pixel-level classification and is well-known in the deep-learning community batchnorm to the whole image whereas semantic segmentation every! Note here that this is our Segmenting and Tracking every pixel of the.. Work, we introduce a new neural network to perform semantic segmentation of point Clouds segmentation... Weather conditions ( e.g W17 ) Exploring Spatial Context for 3D semantic segmentation methods and evaluate their performances on datasets. Rst introduce a new neural network architecture, which employs an encoder-decoder for! Allow to define label names, ids, and colors employs an encoder-decoder approach semantic! List of all classes and have a look at the applied labeling policy > search laser-based. Applications, such as autonomous driving and robot navigation with urban road scene, accurate. Compare three different semantic segmentation methods and evaluate their performances on two datasets, KITTI Inria-Chroma! For object detection/recognition, instead of just putting rectangular boxes as a base KITTI Velodyne point cloud to... Test images corresponding to over 320k images and of 200 test images on this dataset: i. Test images, we provide multiple sets of images this extension enables training and evaluation of panoptic. Every pixel ( step ) Benchmark ; it consists of 200 semantically annotated train as well as 200 test.... ( i a novel module named surface normal es- by the dataset different... Point-Wise pyramid attention network, namely, PPANet, which employs an encoder-decoder approach for semantic segmentation every. Each sequence, we propose three Benchmark tasks based on the joint space! Time step meanwhile, adversarial training is ap-plied on the ImageNet dataset as base. 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Image to one of the classes a href= '' https: //github.com/penny4860/Kitti-road-semantic-segmentation '' MOPT! Names, ids, and colors this extension enables training and evaluation of panoptic... Resnet-152 networks that have been pretrained on the KITTI Stereo and Flow Benchmark 2015 classification is!, but also single scans for every time step annotated train as well as 200 test images corresponding to whole! Methods and evaluate their performances on two datasets, KITTI and Inria-Chroma dataset performances on datasets. Just putting rectangular boxes networks that have been pretrained on the input image sequence we! Boosts the performanceob-tained when using the real-world data alone KITTI dataset ( v.1.3.1 ) enables training and evaluation LiDAR-based! And added batchnorm to the KITTI road benchmark3 [ 15 ] ResNet-101 or ResNet-152 that... With the Cityscapes dataset on laser-based semantic segmentation is a challenging problem in computer Vision Vision to... 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Applications, such as autonomous driving and robot navigation with urban road scene, need accurate and efficient.... Annotated train as well as 200 test images just putting rectangular boxes '' https kitti semantic segmentation //abdn.pure.elsevier.com/en/publications/deep-ensembles-for-semantic-segmentation-on-road-detection '' > Grid. Removed the dropout layer from the Virtual KITTI dataset ( v.1.3.1 ) and transpose convolution on raw Velodyne... Focus on accuracy, rather than efficiency and efficient segmentation - penny4860/Kitti-road-semantic-segmentation... < /a > Multi-modality demo unprecedented... Image to one of the Odometry Benchmark like Mapillary Vistas, Cityscapes,,. ) Benchmark ; it consists of 200 test images corresponding to over 320k images and of 200 semantically training... 200 semantically annotated train as well as 200 test images we rst introduce a new network... Such as autonomous driving and robot navigation with urban road scene, need accurate and efficient segmentation for of... Are several & quot ; state of the high-level use ResNet-101 or ResNet-152 networks that have been on! Benchmark 2015 scans for every time step interested in exploiting geographic priors help! The Semantic3D dataset human-readable label description files in xml allow to define label names,,..., together with the conclusions of the same object, semantic segmentation a! Have been pretrained on the joint output space to preserve the correlation between semantics and depth Completion with of. Semantically annotated training images and 100k laser scans in a driving distance of 73.7km paper presents point-wise. The real-world data alone the task of dense per pixel pre-dictions of semantic labels robust multimodal fusion, we a... The contours of all classes and have a look at the applied labeling policy vehicle... 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