DrawingProcess
๋“œํ”„ DrawingProcess
DrawingProcess
์ „์ฒด ๋ฐฉ๋ฌธ์ž
์˜ค๋Š˜
์–ด์ œ
ยซ   2025/06   ยป
์ผ ์›” ํ™” ์ˆ˜ ๋ชฉ ๊ธˆ ํ† 
1 2 3 4 5 6 7
8 9 10 11 12 13 14
15 16 17 18 19 20 21
22 23 24 25 26 27 28
29 30
  • ๋ถ„๋ฅ˜ ์ „์ฒด๋ณด๊ธฐ (967) N
    • Profile & Branding (25) N
      • Career (18) N
    • IT Trends (254)
      • Conference, Faire (Experien.. (31)
      • News (187)
      • Youtube (19)
      • TED (8)
      • Web Page (2)
      • IT: Etc... (6)
    • Contents (97)
      • Book (66)
      • Lecture (31)
    • Project Process (94)
      • Ideation (0)
      • Study Report (34)
      • Challenge & Award (22)
      • 1Day1Process (5)
      • Making (5)
      • KRC-FTC (Team TC(5031, 5048.. (10)
      • GCP (GlobalCitizenProject) (15)
    • Study: ComputerScience(CS) (72)
      • CS: Basic (9)
      • CS: Database(SQL) (5)
      • CS: Network (14)
      • CS: OperatingSystem (3)
      • CS: Linux (39)
      • CS: Etc... (2)
    • Study: Software(SW) (95)
      • SW: Language (29)
      • SW: Algorithms (1)
      • SW: DataStructure & DesignP.. (1)
      • SW: Opensource (15)
      • SW: Error Bug Fix (43)
      • SW: Etc... (6)
    • Study: Artificial Intellige.. (149)
      • AI: Research (1)
      • AI: 2D Vision(Det, Seg, Tra.. (35)
      • AI: 3D Vision (70)
      • AI: MultiModal (3)
      • AI: SLAM (0)
      • AI: Light Weight(LW) (3)
      • AI: Data Pipeline (7)
      • AI: Machine Learning(ML) (1)
    • Study: Robotics(Robot) (33)
      • Robot: ROS(Robot Operating .. (9)
      • Robot: Positioning (8)
      • Robot: Planning & Control (7)
    • Study: DeveloperTools(DevTo.. (83)
      • DevTool: Git (12)
      • DevTool: CMake (13)
      • DevTool: NoSQL(Elastic, Mon.. (25)
      • DevTool: Container (17)
      • DevTool: IDE (11)
      • DevTool: CloudComputing (4)
    • ์ธ์ƒ์„ ์‚ด๋ฉด์„œ (64)
      • ๋‚˜์˜ ์ทจ๋ฏธ๋“ค (7)
      • ๋‚˜์˜ ์ƒ๊ฐ๋“ค (42)
      • ์—ฌํ–‰์„ ๋– ๋‚˜์ž~ (10)
      • ๋ถ„๊ธฐ๋ณ„ ํšŒ๊ณ  (5)

๊ฐœ๋ฐœ์ž ๋ช…์–ธ

โ€œ ๋งค์ฃผ ๋ชฉ์š”์ผ๋งˆ๋‹ค ๋‹น์‹ ์ด ํ•ญ์ƒ ํ•˜๋˜๋Œ€๋กœ ์‹ ๋ฐœ๋ˆ์„ ๋ฌถ์œผ๋ฉด ์‹ ๋ฐœ์ด ํญ๋ฐœํ•œ๋‹ค๊ณ  ์ƒ๊ฐํ•ด๋ณด๋ผ.
์ปดํ“จํ„ฐ๋ฅผ ์‚ฌ์šฉํ•  ๋•Œ๋Š” ์ด๋Ÿฐ ์ผ์ด ํ•ญ์ƒ ์ผ์–ด๋‚˜๋Š”๋ฐ๋„ ์•„๋ฌด๋„ ๋ถˆํ‰ํ•  ์ƒ๊ฐ์„ ์•ˆ ํ•œ๋‹ค. โ€

- Jef Raskin

๋งฅ์˜ ์•„๋ฒ„์ง€ - ์• ํ”Œ์ปดํ“จํ„ฐ์˜ ๋งคํ‚จํ† ์‹œ ํ”„๋กœ์ ํŠธ๋ฅผ ์ฃผ๋„

์ธ๊ธฐ ๊ธ€

์ตœ๊ทผ ๊ธ€

์ตœ๊ทผ ๋Œ“๊ธ€

ํ‹ฐ์Šคํ† ๋ฆฌ

hELLO ยท Designed By ์ •์ƒ์šฐ.
DrawingProcess

๋“œํ”„ DrawingProcess

[Dataset] Autonomous Driving Open Dataset: nuScenes Dataset(+ nuImages, nuPlan, Occupancy, nuReality)
Study: Artificial Intelligence(AI)/AI: Data Pipeline

[Dataset] Autonomous Driving Open Dataset: nuScenes Dataset(+ nuImages, nuPlan, Occupancy, nuReality)

2023. 12. 26. 18:22
๋ฐ˜์‘ํ˜•
๐Ÿ’ก ๋ณธ ๋ฌธ์„œ๋Š” 'Autonomous Driving Open Dataset: nuScenes Dataset'์— ๋Œ€ํ•ด ์ •๋ฆฌํ•ด๋†“์€ ๊ธ€์ž…๋‹ˆ๋‹ค.
์ž์œจ์ฃผํ–‰ ์ฐจ๋Ÿ‰์˜ ์„ผ์„œ ๋ฐ์ดํ„ฐ์…‹ ์ค‘ ํ•˜๋‚˜์ธ nuScenes Dataset์— ๋Œ€ํ•ด ์ •๋ฆฌํ•˜์˜€์œผ๋‹ˆ ์ฐธ๊ณ ํ•˜์‹œ๊ธฐ ๋ฐ”๋ž๋‹ˆ๋‹ค.

nuScenes Dataset

1) Sensor ๊ตฌ์„ฑ

camera 6๊ฐœ + lidar 1๊ฐœ + radar 5๊ฐœ

\

nuScenes dataset์€ 2019๋…„์— ๊ณต๊ฐœ๋œ ์˜คํ”ˆ๋ฐ์ดํ„ฐ๋กœ detection, tracking, prediction & localization task์„ ์ง€์›ํ•˜๋Š” multi modal dataset์ž…๋‹ˆ๋‹ค. ๋‹จ์ˆœํžˆ image๋งŒ ์ œ๊ณตํ•˜๋Š” ๊ฒƒ์ด ์•„๋‹Œ camera๋กœ ์ˆ˜์ง‘ํ•œ image, Lidar๋กœ ์ˆ˜์ง‘ํ•œ point cloud, radar๋กœ ์ˆ˜์ง‘ํ•œ point cloud ๋“ฑ์ด ์ œ๊ณต๋ฉ๋‹ˆ๋‹ค.

dataset์•ˆ์—๋Š” 140๋งŒ ๊ฐœ์˜ ์นด๋ฉ”๋ผ ์ด๋ฏธ์ง€, 39๋งŒ ๊ฐœ์˜ ๋ผ์ด๋‹ค ์ •๋ณด, 140๋งŒ ๊ฐœ์˜ ๋ ˆ์ด๋” ์ •๋ณด, 140๋งŒ ๊ฐœ์˜ object bounding box๊ฐ€ ํฌํ•จ๋˜์–ด ์žˆ์Šต๋‹ˆ๋‹ค. ์ „์ฒด์ ์ธ ๋ฐ์ดํ„ฐ์˜ ๊ตฌ์„ฑ์€ ๋‹ค์Œ๊ณผ ๊ฐ™์Šต๋‹ˆ๋‹ค.

2) A Introduction to nuScenes

In this part of the tutorial, let us go through a top-down introduction of our database. Our dataset comprises of elemental building blocks that are the following:

  1. log - Log information from which the data was extracted.
  2. scene - 20 second snippet of a car's journey.
  3. sample - An annotated snapshot of a scene at a particular timestamp.
  4. sample_data - Data collected from a particular sensor.
  5. ego_pose - Ego vehicle poses at a particular timestamp.
  6. sensor - A specific sensor type.
  7. calibrated sensor - Definition of a particular sensor as calibrated on a particular vehicle.
  8. instance - Enumeration of all object instance we observed.
  9. category - Taxonomy of object categories (e.g. vehicle, human).
  10. attribute - Property of an instance that can change while the category remains the same.
  11. visibility - Fraction of pixels visible in all the images collected from 6 different cameras.
  12. sample_annotation - An annotated instance of an object within our interest.
  13. map - Map data that is stored as binary semantic masks from a top-down view.

The database schema is visualized below. For more information see the nuScenes schema page. 

3) nuScenes Schema

nuScenes์—์„œ detection์—์„œ ์‚ฌ์šฉํ•˜๋Š” class๋Š” ์ด 10๊ฐ€์ง€๋กœ Car, Bus, Bicycle, Barrier, Construction_vehicle, Motorcycle, Pedestrian, Traffic_cone, Trailer, Truck ์ž…๋‹ˆ๋‹ค.

annotation์„ ํ•œ ๊ธฐ์ค€์„ ์‚ดํŽด๋ณด๋ฉด

  • ๋ฌผ์ฒด๋Š” ์œ„์น˜์™€ ๋ชจ์–‘์„ ์•Œ ์ˆ˜ ์žˆ๋„๋ก ์ ์–ด๋„ LiDAR๋‚˜ Radar point 1๊ฐœ๊ฐ€ ํฌํ•จ๋˜์–ด์•ผ ํ•ฉ๋‹ˆ๋‹ค.
  • ๋ฌผ์ฒด๋ฅผ ๋‚˜ํƒ€๋‚ด๋Š” ์ง์œก๋ฉด์ฒด๋Š” ๋งค์šฐ tightํ•ด์•ผํ•ฉ๋‹ˆ๋‹ค.
  • ๋ฌผ์ฒด์™€ ๋๊ณผ ๋์€ ๋ชจ๋‘ ํฌํ•จ๋˜์–ด์•ผ ํ•ฉ๋‹ˆ๋‹ค.
    → ๋ฌผ์ฒด๊ฐ€ ์ด์–ด์ ธ์žˆ๋‹ค๋ฉด ๋Š๊ธฐ์ง€ ์•Š๊ณ  ํ•˜๋‚˜์— ์ „์ฒด๋ฅผ ํฌํ•จํ•ด์•ผ ํ•ฉ๋‹ˆ๋‹ค.
  • ๋ณดํ–‰์ž๊ฐ€ ์šด๋ฐ˜ํ•˜๋Š” ๋ฌผ๊ฑด๋„ ํฌํ•จํ•ฉ๋‹ˆ๋‹ค.
  • ๊ฐ„ํ˜น ์›€์ง์ด์ง€ ์•Š๋Š” ๋ฌผ์ฒด๊ฐ€ ์›€์ง์ด๋Š” ๊ฒฝ์šฐ ์—๋Ÿฌ๋ฅผ ๋ฐฉ์ง€ํ•˜๊ธฐ ์œ„ํ•ด ๋ณ„๋„์˜ bbox๋ฅผ ๋งŒ๋“ญ๋‹ˆ๋‹ค.
  • LiDAR๋‚˜ Radar์— ์ž˜ ์ฐํžˆ์ง€ ์•Š๋Š” ๋ฌผ์ฒด๋Š” ์นด๋ฉ”๋ผ ์ด๋ฏธ์ง€๋ฅผ ํ†ตํ•ด ํฌ๊ธฐ๋ฅผ ํŒ๋ณ„ํ•ฉ๋‹ˆ๋‹ค.
  • ๋ชจ๋“  ์นด๋ฉ”๋ผ๊ฐ€ ๋ณผ ์ˆ˜ ์žˆ๋Š” ๋ฌผ์ฒด๋Š” ํŠน๋ณ„ํ•œ ์†์„ฑ์„ ๋ถ€์—ฌํ•ฉ๋‹ˆ๋‹ค.

์ž์„ธํ•œ data์˜ ๋‚ด์šฉ์€ nuScenes ๋…ผ๋ฌธ์ด๋‚˜ ํ™ˆํŽ˜์ด์ง€๋ฅผ ๊ฐ€๋ฉด ํ™•์ธํ•  ์ˆ˜ ์žˆ๊ณ  ๋ฐ์ดํ„ฐ๋ฅผ ํ™•์ธํ•ด๋ณด๊ณ  ์‹ถ๋‹ค๋ฉด ์•„๋ž˜์˜ tutorial์„ ์ง„ํ–‰ํ•ด๋ณด์‹œ๊ธธ ๋ฐ”๋ž๋‹ˆ๋‹ค.

  • [Official] nuScenes Dataset tutorial: https://www.nuscenes.org/tutorials/nuscenes_tutorial.html

nuScenes Download Scripts

#!/usr/bin/bash

mkdir done_unzipping

# The download links may change over time.
wget https://d36yt3mvayqw5m.cloudfront.net/public/v1.0/v1.0-trainval_meta.tgz
wget https://motional-nuscenes.s3.amazonaws.com/public/v1.0/v1.0-trainval01_blobs.tgz
wget https://motional-nuscenes.s3.amazonaws.com/public/v1.0/v1.0-trainval02_blobs.tgz
wget https://d36yt3mvayqw5m.cloudfront.net/public/v1.0/v1.0-trainval03_blobs.tgz
wget https://motional-nuscenes.s3.amazonaws.com/public/v1.0/v1.0-trainval04_blobs.tgz
wget https://d36yt3mvayqw5m.cloudfront.net/public/v1.0/v1.0-trainval05_blobs.tgz
wget https://d36yt3mvayqw5m.cloudfront.net/public/v1.0/v1.0-trainval06_blobs.tgz
wget https://d36yt3mvayqw5m.cloudfront.net/public/v1.0/v1.0-trainval07_blobs.tgz
wget https://motional-nuscenes.s3.amazonaws.com/public/v1.0/v1.0-trainval08_blobs.tgz
wget https://motional-nuscenes.s3.amazonaws.com/public/v1.0/v1.0-trainval09_blobs.tgz
wget https://motional-nuscenes.s3.amazonaws.com/public/v1.0/v1.0-trainval10_blobs.tgz
wget https://motional-nuscenes.s3.amazonaws.com/public/v1.0/v1.0-test_blobs.tgz
wget https://d36yt3mvayqw5m.cloudfront.net/public/v1.0/v1.0-trainval_meta.tgz

tar -xzvf v1.0-trainval_meta.tgz && mv v1.0-trainval_meta.tgz ./done_unzipping/
tar -xzvf v1.0-trainval01_blobs.tgz && mv v1.0-trainval01_blobs.tgz ./done_unzipping/
tar -xzvf v1.0-trainval02_blobs.tgz && mv v1.0-trainval02_blobs.tgz ./done_unzipping/
tar -xzvf v1.0-trainval03_blobs.tgz && mv v1.0-trainval03_blobs.tgz ./done_unzipping/
tar -xzvf v1.0-trainval04_blobs.tgz && mv v1.0-trainval04_blobs.tgz ./done_unzipping/
tar -xzvf v1.0-trainval05_blobs.tgz && mv v1.0-trainval05_blobs.tgz ./done_unzipping/
tar -xzvf v1.0-trainval06_blobs.tgz && mv v1.0-trainval06_blobs.tgz ./done_unzipping/
tar -xzvf v1.0-trainval07_blobs.tgz && mv v1.0-trainval07_blobs.tgz ./done_unzipping/
tar -xzvf v1.0-trainval08_blobs.tgz && mv v1.0-trainval08_blobs.tgz ./done_unzipping/
tar -xzvf v1.0-trainval09_blobs.tgz && mv v1.0-trainval09_blobs.tgz ./done_unzipping/
tar -xzvf v1.0-trainval10_blobs.tgz && mv v1.0-trainval10_blobs.tgz ./done_unzipping/
tar -xzvf v1.0-test_blobs.tgz && mv v1.0-test_blobs.tgz ./done_unzipping/
tar -xzvf v1.0-trainval_meta.tgz && mv v1.0-trainval_meta.tgz ./done_unzipping/

 

nuScenes Related Source

  • [colab] nutonomy/nuscenes-devkit: https://colab.research.google.com/github/nutonomy/nuscenes-devkit/
  • [Github] nutonomy/nuscenes-devkit: https://github.com/nutonomy/nuscenes-devkit
  • [Github] chiyukunpeng/nuscenes_viz: https://github.com/chiyukunpeng/nuscenes_viz
  • [Github] clynamen/nuscenes2bag: https://github.com/clynamen/nuscenes2bag

nuScenes Other Dataset

1. nuImages

nuImages is a large-scale autonomous driving dataset with image-level 2d annotations. It features:

  • 93k video clips of 6s each (150h of driving)
  • 93k annotated and 1.1M un-annotated images
  • Two diverse cities: Boston and Singapore
  • The same proven sensor suite as in nuScenes
  • Images mined for diversity
  • 800k annotated foreground objects with 2d bounding boxes and instance masks
  • 100k 2d semantic segmentation masks for background classes
  • Attributes such as rider, pose, activity, emergency lights and flying
  • Free to use for non-commercial use
  • For a commercial license contact nuScenes@motional.com

2. nuPlan

nuPlan is the world's first large-scale planning benchmark for autonomous driving. It features:

  • The world's first ML planning benchmark
  • 1200h of driving data from 4 cities (Boston, Pittsburgh, Las Vegas and Singapore)
  • Sensor data released for 120h (5x LIDAR, 8x camera, IMU, GPS)
  • Left versus right hand traffic
  • Detailed map information
  • 5B 3D bounding boxes auto labeled for 7 classes
  • Open and closed loop planning simulation
  • 30+ mined scenario types (e.g. lane change, unprotected turn, jaywalker)
  • 20+ open and closed loop planning simulation and metrics to score planners (traffic rule violation, human driving similarity, vehicle dynamic, goal achievement)
  • Traffic light statuses inferred from agent movement
  • Baselines and framework to train reactive agents and ML based planners
  • Upcoming challenges around planning and smart agents in 2022
  • Free to use for non-commercial use
  • For a commercial license contact nuPlan@motional.com

3. nuScenes Occupancy

  • [Paperwithcode] Prediction Of Occupancy Grid Maps on Occ3D-nuScenes: https://paperswithcode.com/sota/prediction-of-occupancy-grid-maps-on-occ3d
  • [eval.ai] 3D Occupancy Prediction Challenge: https://eval.ai/web/challenges/challenge-page/2045/overview
  • [archive] NuScenes Occupancy Grids Dataset: https://archive.org/details/nuscenes-occupancy-grids-dataset
  • [Git] Occupancy Dataset for nuScenes: https://github.com/FANG-MING/occupancy-for-nuscenes
  • [Git] CVPR2023-3D-Occupancy-Prediction
    : https://github.com/CVPR2023-3D-Occupancy-Prediction/CVPR2023-3D-Occupancy-Prediction
  • [Git] huang-yh/selfocc: https://github.com/huang-yh/selfocc
  • [Paper] Predicting Future Occupancy Grids in Dynamic Environment with Spatio-Temporal Learning:  https://arxiv.org/pdf/2205.03212v1.pdf
  • [Git] OccupancyGrid-Predictions: https://github.com/ksm26/OccupancyGrid-Predictions 

4. nuReality

 

์ฐธ๊ณ 

  • [Blog] 15 Best Open-Source Autonomous Driving Datasets: https://medium.com/analytics-vidhya/15-best-open-source-autonomous-driving-datasets-34324676c8d7
  • [Official] nuScenes Dataset Overview: https://www.nuscenes.org/nuscenes#overview
  • [Official] nuScenes Dataset Downloads: https://www.nuscenes.org/nuscenes#download
๋ฐ˜์‘ํ˜•
์ €์ž‘์žํ‘œ์‹œ ๋น„์˜๋ฆฌ ๋ณ€๊ฒฝ๊ธˆ์ง€ (์ƒˆ์ฐฝ์—ด๋ฆผ)

'Study: Artificial Intelligence(AI) > AI: Data Pipeline' ์นดํ…Œ๊ณ ๋ฆฌ์˜ ๋‹ค๋ฅธ ๊ธ€

[Data] Python ์ด๋ฏธ์ง€ ์—ฌ๋ฐฑ ์ง€์šฐ๊ธฐ (numpy, mask, ...)  (0) 2024.04.30
[Data] Segmentation ๋ฐ์ดํ„ฐ ์••์ถ• ์•Œ๊ณ ๋ฆฌ์ฆ˜: Run Length Encoding(RLE) - coco mask to rle์™€ rle to mask ๊ฒ€์ฆ๊นŒ์ง€  (0) 2024.02.28
[Deploy] ONNX: ๋‹ค๋ฅธ DNN ํ”„๋ ˆ์ž„์›Œํฌ ๊ฐ„ ๋ชจ๋ธ ํ˜ธํ™˜ ํฌ๋ฉง(pytorch, tensorflow, TensorRT, ...)  (1) 2024.02.14
[Dataset] Autonomous Driving Open Dataset: KITTI Dataset (Visual Odometry/SLAM, 3D Object Detection)  (1) 2023.12.26
[Dataset] Autonomous Driving Open Dataset: Various Datasets  (0) 2023.09.09
    'Study: Artificial Intelligence(AI)/AI: Data Pipeline' ์นดํ…Œ๊ณ ๋ฆฌ์˜ ๋‹ค๋ฅธ ๊ธ€
    • [Data] Segmentation ๋ฐ์ดํ„ฐ ์••์ถ• ์•Œ๊ณ ๋ฆฌ์ฆ˜: Run Length Encoding(RLE) - coco mask to rle์™€ rle to mask ๊ฒ€์ฆ๊นŒ์ง€
    • [Deploy] ONNX: ๋‹ค๋ฅธ DNN ํ”„๋ ˆ์ž„์›Œํฌ ๊ฐ„ ๋ชจ๋ธ ํ˜ธํ™˜ ํฌ๋ฉง(pytorch, tensorflow, TensorRT, ...)
    • [Dataset] Autonomous Driving Open Dataset: KITTI Dataset (Visual Odometry/SLAM, 3D Object Detection)
    • [Dataset] Autonomous Driving Open Dataset: Various Datasets
    DrawingProcess
    DrawingProcess
    ๊ณผ์ •์„ ๊ทธ๋ฆฌ์ž!

    ํ‹ฐ์Šคํ† ๋ฆฌํˆด๋ฐ”