A morphable model for the synthesis of 3D faces

Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment. Most autonomous vehicles, however, carry a combination of cameras and range sensors such as lidar and radar. As machine learning based methods for detection and tracking become more prevalent, there is a need to train and evaluate such methods on datasets containing range sensor data along with images. In this work we present nuTonomy scenes (nuScenes), the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view. nuScenes comprises 1000 scenes, each 20s long and fully annotated with 3D bounding boxes for 23 classes and 8 attributes. It has 7x as many annotations and 100x as many images as the pioneering KITTI dataset. We define novel 3D detection and tracking metrics. We also provide careful dataset analysis as well as baselines for lidar and image based detection and tracking. Data, development kit and more information are available online.

nuScenes: A multimodal dataset for autonomous driving

https://cyberleninka.org/article/n/768408.pdf

Mechanical properties of graphene and graphene-based nanocomposites

/pdf/nuscenes-a-multimodal-dataset-for-autonomous-driving-3znmpkqyuu.pdf

nuScenes: A Multimodal Dataset for Autonomous Driving

A review of PEM fuel cell durability: Degradation mechanisms and mitigation strategies

Autonomous driving has attracted tremendous attention especially in the past few years. The key techniques for a self-driving car include solving tasks like 3D map construction, self-localization, parsing the driving road and understanding objects, which enable vehicles to reason and act. However, large scale data set for training and system evaluation is still a bottleneck for developing robust perception models. In this paper, we present the ApolloScape dataset [1] and its applications for autonomous driving. Compared with existing public datasets from real scenes, e.g., KITTI [2] or Cityscapes [3] , ApolloScape contains much large and richer labelling including holistic semantic dense point cloud for each site, stereo, per-pixel semantic labelling, lanemark labelling, instance segmentation, 3D car instance, high accurate location for every frame in various driving videos from multiple sites, cities and daytimes. For each task, it contains at lease 15x larger amount of images than SOTA datasets. To label such a complete dataset, we develop various tools and algorithms specified for each task to accelerate the labelling process, such as joint 3D-2D segment labeling, active labelling in videos etc. Depend on ApolloScape , we are able to develop algorithms jointly consider the learning and inference of multiple tasks. In this paper, we provide a sensor fusion scheme integrating camera videos, consumer-grade motion sensors (GPS/IMU), and a 3D semantic map in order to achieve robust self-localization and semantic segmentation for autonomous driving. We show that practically, sensor fusion and joint learning of multiple tasks are beneficial to achieve a more robust and accurate system. We expect our dataset and proposed relevant algorithms can support and motivate researchers for further development of multi-sensor fusion and multi-task learning in the field of computer vision.

The ApolloScape Open Dataset for Autonomous Driving and Its Application

Scene parsing aims to assign a class (semantic) label for each pixel in an image. It is a comprehensive analysis of an image. Given the rise of autonomous driving, pixel-accurate environmental perception is expected to be a key enabling technical piece. However, providing a large scale dataset for the design and evaluation of scene parsing algorithms, in particular for outdoor scenes, has been difficult. The per-pixel labelling process is prohibitively expensive, limiting the scale of existing ones. In this paper, we present a large-scale open dataset, ApolloScape, that consists of RGB videos and corresponding dense 3D point clouds. Comparing with existing datasets, our dataset has the following unique properties. The first is its scale, our initial release contains over 140K images - each with its per-pixel semantic mask, up to 1M is scheduled. The second is its complexity. Captured in various traffic conditions, the number of moving objects averages from tens to over one hundred. And the third is the 3D attribute, each image is tagged with high-accuracy pose information at cm accuracy and the static background point cloud has mm relative accuracy. We are able to label these many images by an interactive and efficient labelling pipeline that utilizes the high-quality 3D point cloud. Moreover, our dataset also contains different lane markings based on the lane colors and styles. We expect our new dataset can deeply benefit various autonomous driving related applications that include but not limited to 2D/3D scene understanding, localization, transfer learning, and driving simulation.

The ApolloScape Dataset for Autonomous Driving

The life of proton exchange membrane fuel cells (PEMFC) is currently lim- ited by the mechanical endurance of polymer electrolyte membranes and membrane electrode assemblies (MEAs). In this paper, the authors report recent experimental and modeling work toward understanding the mechanisms of delayed mechanical failures of polymer electrolyte membranes and MEAs under relevant PEMFC operating conditions. Mechanical breach of membranes/MEAs in the form of pinholes and tears has been fre- quently observed after long-term or accelerated testing of PEMFC cells/stacks. Cata- strophic failure of cell/stack due to rapid gas crossover shortly follows the mechanical breach. Ex situ mechanical characterizations were performed on MEAs after being sub- jected to the accelerated chemical aging and relative humidity (RH) cycling tests. The results showed significant reduction of MEA ductility manifested as drastically reduced strain-to-failure of the chemically aged and RH-cycled MEAs. Postmortem analysis re- vealed the formation and growth of mechanical defects such as cracks and crazing in the membranes and MEAs. A finite element model was used to estimate stress/strain states of an edge-constrained MEA under rapid RH variations. Damage metrics for accelerated testing and life prediction of PEMFCs are discussed. V C 2006 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 44: 2346-2357, 2006

Mechanical Endurance of Polymer Electrolyte Membrane and PEM Fuel Cell Durability

Scene parsing aims to assign a class (semantic) label for each pixel in an image. It is a comprehensive analysis of an image. Given the rise of autonomous driving, pixel-accurate environmental perception is expected to be a key enabling technical piece. However, providing a large scale dataset for the design and evaluation of scene parsing algorithms, in particular for outdoor scenes, has been difficult. The per-pixel labelling process is prohibitively expensive, limiting the scale of existing ones. In this paper, we present a large-scale open dataset, ApolloScape, that consists of RGB videos and corresponding dense 3D point clouds. Comparing with existing datasets, our dataset has the following unique properties. The first is its scale, our initial release contains over 140K images - each with its per-pixel semantic mask, up to 1M is scheduled. The second is its complexity. Captured in various traffic conditions, the number of moving objects averages from tens to over one hundred (Figure 1). And the third is the 3D attribute, each image is tagged with high-accuracy pose information at cm accuracy and the static background point cloud has mm relative accuracy. We are able to label these many images by an interactive and efficient labelling pipeline that utilizes the high-quality 3D point cloud. Moreover, our dataset also contains different lane markings based on the lane colors and styles. We expect our new dataset can deeply benefit various autonomous driving related applications that include but not limited to 2D/3D scene understanding, localization, transfer learning, and driving simulation.

/pdf/the-apolloscape-dataset-for-autonomous-driving-3okoqo93yp.pdf

Xinyu Huang

Papers

The ApolloScape Open Dataset for Autonomous Driving and Its Application

The ApolloScape Dataset for Autonomous Driving

Mechanical Endurance of Polymer Electrolyte Membrane and PEM Fuel Cell Durability

The ApolloScape Dataset for Autonomous Driving

Evaluation of coated metallic bipolar plates for polymer electrolyte membrane fuel cells