[!CAUTION] Commercial use of RoScenes is strictly forbidden.
📰 Release Note
[2024-07-14] You can now download the dataset at ModelScope.
[2024-07-13] Devkit for RoScenes released.
[2024-07-01] Paper accepted to ECCV 2024! 🥳
[2024-05-28] Please stay tuned for the updates! We are doing final checks on data privacy.
🏙️ Features
🔖 Table of Contents
🔥 Quick Start
Download
[!NOTE] Please refer to ModelScope for downloading the dataset.
After download and extract, the dataset folder should be organized as follows:
. [DATA_ROOT] # Dataset root folder
├── 📂train # training set
│ ├── 📂s001_split_train_difficulty_mixed_ambience_day # scene 001's data
│ │ ├── 📂database # annotations, grouped by clip
│ │ │ ├── 📂0076fd69_clip_[0000000000000-0000000029529] # a clip's database, please use our devkit to read
│ │ │ └ ...
│ │ └── 📂images # images, grouped by clips
│ │ ├── 📂0076fd69
│ │ └ ...
│ ├── 📂s002_split_train_difficulty_mixed_ambience_day
│ ├── 📂s003_split_train_difficulty_mixed_ambience_day
│ ├── 📂s004_split_train_difficulty_mixed_ambience_day
│ └── 📂night_split_train_difficulty_mixed_ambience_night
│
│
├── 📂validation # validation set
│ ├── 📂s001_split_validation_difficulty_mixed_ambience_day # scene 001's data
│ ├── 📂s002_split_validation_difficulty_mixed_ambience_day
│ ├── 📂s003_split_validation_difficulty_mixed_ambience_day
│ ├── 📂s004_split_validation_difficulty_mixed_ambience_day
│ └── 📂night_split_validation_difficulty_mixed_ambience_night
│
│
└── 📂test # test set
├── 📂NO_GTs005_split_test_difficulty_mixed_ambience_day # scene 005's data
├── 📂NO_GTs006_split_test_difficulty_mixed_ambience_day
├── 📂NO_GTs007_split_test_difficulty_mixed_ambience_day
├── 📂NO_GTs008_split_test_difficulty_mixed_ambience_day
├── 📂NO_GTs009_split_test_difficulty_mixed_ambience_day
├── 📂NO_GTs010_split_test_difficulty_mixed_ambience_day
├── 📂NO_GTs011_split_test_difficulty_mixed_ambience_day
├── 📂NO_GTs012_split_test_difficulty_mixed_ambience_day
├── 📂NO_GTs013_split_test_difficulty_mixed_ambience_day
└── 📂NO_GTs014_split_test_difficulty_mixed_ambience_day
Install via PyPI
Use PyPI to directly install RoScenes devkit:
pip install roscenes
Install Manually (for dev)
Also, you can clone this repository and install roscenes manually for developing.
git clone https://github.com/roscenes/RoScenes.git
cd RoScenes
pip install -e .
Start Using the Dataset
import roscenes as ro
# load the training set
dataset = ro.load('[DATA_ROOT]/train/*')
# number of total frames
print(len(dataset))
Then, we can iterate over the dataset, in two ways:
You can use indexing:
# use integer indexing
index = 10
# a Frame instance
print(type(dataset[index]))
for i in range(len(dataset)):
# print num of objects for every frame
print(len(dataset[index].boxes3D))
OR, you can directly iterate it:
# a frame instance
for frame in dataset:
print(len(frame.boxes3D))
[!IMPORTANT] Please refer to
frame.py,camera.pyfor the detailed comments on box format, instrinsic and extrinsic definition, etc.
🔎 Explore the Dataset
python -m roscenes.visualizer [DATA_ROOT]/train/s001_split_train_difficulty_mixed_ambience_day 0 vis_result
TBD.
👩💻 Examples
- 1. Read all boxes in a frame, and convert them from global 3D coordinates to camera's perspective coordinates.
📈 Evaluation
TBD.
🎯 To-do List
- Devkit release
- Dataset release
- Example dataset loader based on
MMDetection3D - 3D detection task and evaluation suite
- 3D tracking task and evaluation suite
This repo is licensed under
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