3D detection and tracking viewer (visualization) for kitti & waymo dataset

Last update: Jan 08, 2023

Overview

3D Detection & Tracking Viewer

This project was developed for view 3D object detection and tracking results. It supports rendering 3D bounding boxes as car models and rendering boxes on images.

Features

Rendering boxes as cars
Captioning box ids(infos) in 3D scene
Projecting 3D box or points on 2D image

Design pattern

This code includes two parts, one for data loading, other one for visualization of 3D detection and tracking results. The overall framework of design is as shown below:

Prepare data

Kitti detection dataset

# For Kitti Detection Dataset         
└── kitti_detection
       ├── testing 
       |      ├──calib
       |      ├──image_2
       |      ├──label_2
       |      └──velodyne      
       └── training
              ├──calib
              ├──image_2
              ├──label_2
              └──velodyne

Kitti tracking dataset

# For Kitti Tracking Dataset         
└── kitti_tracking
       ├── testing 
       |      ├──calib
       |      |    ├──0000.txt
       |      |    ├──....txt
       |      |    └──0028.txt
       |      ├──image_02
       |      |    ├──0000
       |      |    ├──....
       |      |    └──0028
       |      ├──label_02
       |      |    ├──0000.txt
       |      |    ├──....txt
       |      |    └──0028.txt
       |      └──velodyne
       |           ├──0000
       |           ├──....
       |           └──0028      
       └── training # the structure is same as testing set
              ├──calib
              ├──image_02
              ├──label_02
              └──velodyne

Waymo dataset

Please refer to the OpenPCDet for Waymo dataset organization.

Requirements

python3
numpy
vedo
vtk
opencv
matplotlib

Usage

1. Set boxes type & viewer background color

Currently this code supports Kitti (h,w,l,x,y,z,yaw) and Waymo OpenPCDet (x,y,z,l,w,h,yaw) box type. You can set the box type and background color when initializing a viewer as

from viewer.viewer import Viewer

vi = Viewer(box_type="Kitti",bg = (255,255,255))

2. Set objects color map

You can set the objects color map for view tracking results, same as matplotlab.pypot color map. The common used color maps are "rainbow", "viridis","brg","gnuplot","hsv" and etc.

vi.set_ob_color_map('rainbow')

3. Add colorized point clouds to 3D scene

The viewer receive a set of points, it must be a array with shape (N,3). If you want to view the scatter filed, you should to set the 'scatter_filed' with a shape (N,), and set the 'color_map_name' to specify the colors. If the 'scatter_filed' is None, the points will show in color of 'color' arg.

vi.add_points(points[:,0:3],
               radius = 2,
               color = (150,150,150),
               scatter_filed=points[:,2],
               alpha=1,
               del_after_show='True',
               add_to_3D_scene = True,
               add_to_2D_scene = True,
               color_map_name = "viridis")

4. Add boxes or cars to 3D scene

The viewer receive a set of boxes, it must be a array with shape (N,7). You can set the boxes to meshes or lines only, you also can set the line width, conner points. Besides, you can provide a set of IDs(int) to colorize the boxes, and put a set of additional infos to caption the boxes. Note that, the color will set to the color of "color" arg if the ids is None.

vi.add_3D_boxes(boxes=boxes[:,0:7],
                 ids=ids,
                 box_info=infos,
                 color="blue",
                 add_to_3D_scene=True,
                 mesh_alpha = 0.3,
                 show_corner_spheres = True,
                 corner_spheres_alpha = 1,
                 corner_spheres_radius=0.1,
                 show_heading = True,
                 heading_scale = 1,
                 show_lines = True,
                 line_width = 2,
                 line_alpha = 1,
                 show_ids = True,
                 show_box_info=True,
                 del_after_show=True,
                 add_to_2D_scene=True,
                 caption_size=(0.05,0.05)
                 )

You can also render the boxes as cars, the input format is same as boxes.

vi.add_3D_cars(boxes=boxes[:,0:7],
                 ids=ids,
                 box_info=infos,
                 color="blue",
                 mesh_alpha = 1,
                 show_ids = True,
                 show_box_info=True,
                 del_after_show=True,
                 car_model_path="viewer/car.obj",
                 caption_size = (0.1, 0.1)
                )

5. View boxes or points on image

To view the 3D box and points on image, firstly should set the camera intrinsic, extrinsic mat, and put a image. Besides, when adding the boxes and points, the 'add_to_2D_scene' should be set to True.

vi.add_image(image)
vi.set_extrinsic_mat(V2C)
vi.set_intrinsic_mat(P2)

6. Show 2D and 3D results

To show a single frame, you can directly run vi.show_2D(), vi.show_3D(). The visualization window will not close until you press the "Enter" key. Please zoom out the 3D scene by scrolling the middle mouse button backward, and then you can see the point cloud in this window. You can change the viewing angle by dragging the mouse within the visualization window.

To show multiple frames, you can use the for loop, and press the "Enter" key to view a sequence data.

for i in range(len(dataset)):
    V2C, P2, image, boxes = dataset[i]
    vi.add_3D_boxes(boxes)
    vi.add_image(image)
    vi.set_extrinsic_mat(V2C)
    vi.set_intrinsic_mat(P2)
    vi.show_2D()
    vi.show_3D()

3D detection and tracking viewer (visualization) for kitti & waymo dataset

Related tags

Overview

3D Detection & Tracking Viewer

Features

Design pattern

Prepare data

Requirements

Usage

1. Set boxes type & viewer background color

2. Set objects color map

3. Add colorized point clouds to 3D scene

4. Add boxes or cars to 3D scene

5. View boxes or points on image

6. Show 2D and 3D results

Owner

This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" on Object Detection and Instance Segmentation.

[Preprint] "Bag of Tricks for Training Deeper Graph Neural Networks A Comprehensive Benchmark Study" by Tianlong Chen, Kaixiong Zhou, Keyu Duan, Wenqing Zheng, Peihao Wang, Xia Hu, Zhangyang Wang

Official Pytorch implementation of "CLIPstyler:Image Style Transfer with a Single Text Condition"

Certified Patch Robustness via Smoothed Vision Transformers

Code for reproducible experiments presented in KSD Aggregated Goodness-of-fit Test.

Source code for the Paper: CombOptNet: Fit the Right NP-Hard Problem by Learning Integer Programming Constraints}

Pip-package for trajectory benchmarking from "Be your own Benchmark: No-Reference Trajectory Metric on Registered Point Clouds", ECMR'21

Improving Query Representations for DenseRetrieval with Pseudo Relevance Feedback:A Reproducibility Study.

An LSTM for time-series classification

Non-Homogeneous Poisson Process Intensity Modeling and Estimation using Measure Transport

High frequency AI based algorithmic trading module.

基于Flask开发后端、VUE开发前端框架，在WEB端部署YOLOv5目标检测模型

S2s2net - Sentinel-2 Super-Resolution Segmentation Network

Open source implementation of "A Self-Supervised Descriptor for Image Copy Detection" (SSCD).

Scikit-event-correlation - Event Correlation and Forecasting over High Dimensional Streaming Sensor Data algorithms

Source codes for "Structure-Aware Abstractive Conversation Summarization via Discourse and Action Graphs"

Barbershop: GAN-based Image Compositing using Segmentation Masks (SIGGRAPH Asia 2021)

Differentiable Neural Computers, Sparse Access Memory and Sparse Differentiable Neural Computers, for Pytorch

YolactEdge: Real-time Instance Segmentation on the Edge

Equipped customers with insights about their EVs Hourly energy consumption and helped predict future charging behavior using LSTM model

3D detection and tracking viewer (visualization) for kitti & waymo dataset

Related tags

Overview

3D Detection & Tracking Viewer

Features

Design pattern

Prepare data

Requirements

Usage

1. Set boxes type & viewer background color

2. Set objects color map

3. Add colorized point clouds to 3D scene

4. Add boxes or cars to 3D scene

5. View boxes or points on image

6. Show 2D and 3D results

Owner

This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" on Object Detection and Instance Segmentation.

[Preprint] "Bag of Tricks for Training Deeper Graph Neural Networks A Comprehensive Benchmark Study" by Tianlong Chen*, Kaixiong Zhou*, Keyu Duan, Wenqing Zheng, Peihao Wang, Xia Hu, Zhangyang Wang

Official Pytorch implementation of "CLIPstyler:Image Style Transfer with a Single Text Condition"

Certified Patch Robustness via Smoothed Vision Transformers

Code for reproducible experiments presented in KSD Aggregated Goodness-of-fit Test.

Source code for the Paper: CombOptNet: Fit the Right NP-Hard Problem by Learning Integer Programming Constraints}

Pip-package for trajectory benchmarking from "Be your own Benchmark: No-Reference Trajectory Metric on Registered Point Clouds", ECMR'21

Improving Query Representations for DenseRetrieval with Pseudo Relevance Feedback:A Reproducibility Study.

An LSTM for time-series classification

Non-Homogeneous Poisson Process Intensity Modeling and Estimation using Measure Transport

High frequency AI based algorithmic trading module.

基于Flask开发后端、VUE开发前端框架，在WEB端部署YOLOv5目标检测模型

S2s2net - Sentinel-2 Super-Resolution Segmentation Network

Open source implementation of "A Self-Supervised Descriptor for Image Copy Detection" (SSCD).

Scikit-event-correlation - Event Correlation and Forecasting over High Dimensional Streaming Sensor Data algorithms

Source codes for "Structure-Aware Abstractive Conversation Summarization via Discourse and Action Graphs"

Barbershop: GAN-based Image Compositing using Segmentation Masks (SIGGRAPH Asia 2021)

Differentiable Neural Computers, Sparse Access Memory and Sparse Differentiable Neural Computers, for Pytorch

YolactEdge: Real-time Instance Segmentation on the Edge

Equipped customers with insights about their EVs Hourly energy consumption and helped predict future charging behavior using LSTM model

[Preprint] "Bag of Tricks for Training Deeper Graph Neural Networks A Comprehensive Benchmark Study" by Tianlong Chen, Kaixiong Zhou, Keyu Duan, Wenqing Zheng, Peihao Wang, Xia Hu, Zhangyang Wang