1 # Copyright (c) 2018-2019 NVIDIA CORPORATION. All rights reserved.
3 @page intermediate_tutorials Intermediate Tutorials
6 These intermediate tutorials are intended for users already familiar with DriveWorks and are able to build simple applications.<br>
7 This is a deep dive into most of the functionalities provided, including image and point cloud processing, Deep Neural Networks (DNN) usage,<br>
8 egomotion and self-calibration.
10 @section dwx_intermediate_tutorials_sensors Sensor Management
12 @note The following tutorials are applicable to modules in both **NVIDIA DriveWorks** and **NVIDIA DRIVE Software** releases.
14 | Tutorial | Description |
15 |------|-------------|
16 | @ref sensormanager_usecase1 | Accessing time-sorted stream of events from an aggregate set of sensors. |
18 **Modules used:** @ref core_mainsection | @ref sensors_mainsection | @ref sensormanager_mainsection | @ref rig_mainsection.
22 @section dwx_intermediate_tutorials_ip Image Processing
24 @note The following tutorials are applicable to modules in both **NVIDIA DriveWorks** and **NVIDIA DRIVE Software** releases.
26 | Tutorial | Description |
27 |------|-------------|
28 | @ref image_usecase5 | Creating and converting images into different formats. |
29 | @ref imagetransformation_usecase1 | Scaling images. |
30 | @ref image_usecase2 | Your first image streamer. |
31 | @ref image_usecase4 | Streaming images between threads. |
32 | @ref image_usecase3 | Streaming images across processes. |
33 | @ref image_usecase1 | Capturing images from screen. |
34 | @ref colorcorrection_usecase1 | Adjusting Color Distribution. |
35 | @ref connectedcomponents_usecase1 | Applying Component Labeling to Grey Scale Images. |
36 | @ref rectifier_usecase1 | Converting images to a different camera model. |
37 | @ref imageprocessing_features_usecase1 | Tracking features across frames. |
38 | @ref imageprocessing_tracking_usecase1 | Tracking the position and size of features across frames. |
39 | @ref imageprocessing_tracking_usecase2 | Tracking 2D bounding boxes across frames. |
40 | @ref stereo_usecase1 | Computing a disparity map from a pair of stereo images. |
41 | @ref stereo_usecase2 | Computing a disparity map from a pair of stereo images on PVA and NVENC. |
42 | @ref sfm_usecase1 | Estimating the structure of a scene from camera frames. |
43 | @ref denseopticalflow_usecase1 | Building a pipeline running on PVA. |
45 **Modules used:** @ref image_mainsection | @ref rig_mainsection | @ref colorcorrection_mainsection | @ref connectedcomponents_mainsection | @ref rectifier_mainsection <br> @ref imageprocessing_features_mainsection | @ref imageprocessing_tracking_mainsection | @ref imageprocessing_filtering_mainsection | @ref stereo_mainsection | @ref sfm_mainsection | @ref denseopticalflow_mainsection.
49 @section dwx_intermediate_tutorials_pc_processing Point Cloud Processing
51 @note The following tutorials are applicable to modules in both **NVIDIA DriveWorks** and **NVIDIA DRIVE Software** releases.
53 | Tutorial | Description |
54 |------|-------------|
55 | @ref pointcloudprocessing_usecase1 | Allocating and freeing memory for low level point cloud processing. |
56 | @ref pointcloudprocessing_usecase2 | Accumulating Lidar spins. |
57 | @ref pointcloudprocessing_usecase3 | Combining multiple point clouds in the a common coordinate system. |
58 | @ref pointcloudprocessing_usecase4 | Generating 2D images from accumulated point clouds. |
59 | @ref pointcloudprocessing_usecase5 | Aligning 3D points from a pair of Lidar spins. |
60 | @ref pointcloudprocessing_usecase6 | Estimating a 3D plane from a point cloud. |
61 | @ref pointcloudprocessing_usecase7 | Point cloud filter. |
63 **Modules used:** @ref pointcloudprocessing_mainsection.
67 @section dwx_intermediate_tutorials_vehicle Vehicle Actuation
69 @note The following tutorials are applicable to modules in both **NVIDIA DriveWorks** and **NVIDIA DRIVE Software** releases.
71 | Tutorial | Description |
72 |------|-------------|
73 | @ref vehicleio_usecase1 | How to actuate a vehicle. |
74 | @ref egomotion_usecase1 | How to track and predict a vehicle's pose. |
76 **Modules used:** @ref vehicleio_mainsection | @ref egomotion_mainsection
80 @section dwx_intermediate_tutorials_selfcalib Self-Calibration
82 @note The following tutorials are applicable to modules in both **NVIDIA DriveWorks** and **NVIDIA DRIVE Software** releases.
84 | Tutorial | Description |
85 |------|-------------|
86 | @ref cameramodel_usecase0 | How to perform Ray-to-Pixel and Pixel-to-Ray transformations. |
87 | @ref calibration_usecase_features | A complete workflow detailing camera self-calibration. |
88 | @ref calibration_usecase_imu | A complete workflow detailing IMU self-calibration. |
89 | @ref calibration_usecase_lidar | A complete workflow detailing Lidar self-calibration. |
90 | @ref calibration_usecase_radar | A complete workflow detailing Radar self-calibration. |
91 | @ref calibration_usecase_stereo | A complete workflow detailing epi-polar based stereo self-calibration. |
92 | @ref calibration_usecase_vehicle | A complete workflow detailing steering system properities self-calibration. |
94 **Modules used:** @ref cameramodel_mainsection | @ref calibration_mainsection.
98 @section dwx_intermediate_tutorials_dnn Deep Neural Networks (DNN)
100 @note The following tutorials are applicable to modules in NVIDIA DRIVE Software releases only, with the exception of @ref dataconditioner_usecase1, @ref dnn_usecase1, @ref dnn_usecase2,<br> @ref dnn_usecase3, and @ref clusterer_usecase1.
102 | Tutorial | Description |
103 |------|-------------|
104 | @ref dataconditioner_usecase1 | Formatting your data. |
105 | @ref dnn_usecase1 | The DriveWorks DNN framework. |
106 | @ref dnn_usecase2 | DNN inference using tensors. |
107 | @ref dnn_usecase3 | DNN inference using safe DLA. |
108 | @ref clusterer_usecase1 | A DBSCAN implementation. |
109 | @ref drivenet_usecase1 | A deep neural network trained for object detection. |
110 | @ref openroadnet_usecase1 | A deep neural network trained for freespace detection. |
111 | @ref mapnet_usecase1 | A deep neural network trained for landmark detection. |
112 | @ref parknet_usecase1 | A deep neural network trained for parking space detection. |
113 | @ref pathnet_usecase1 | A deep neural network trained for path detection. |
114 | @ref pilotnet_usecase1 | A deep neural network trained for predicting paths for maneuvers. |
115 | @ref signnet_usecase1 | A deep neural network trained for traffic sign classification. |
116 | @ref lightnet_usecase1 | A deep neural network trained for traffic light classification. |
117 | @ref clearsightnet_usecase1 | A deep neural network trained for camera occlusion detection. |
119 **Modules used:** @ref dnn_mainsection | @ref dataconditioner_mainsection | @ref clusterer_mainsection | @ref drivenet_mainsection | @ref openroadnet_mainsection | @ref mapnet_mainsection | @ref pathnet_mainsection | @ref signnet_mainsection | @ref lightnet_mainsection | @ref pathnet_mainsection @ref clearsightnet_mainsection.