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DEV-Reals/configs/enslamdata5 | hf://datasets/delinqu/EN-SLAM-Dataset@bfbecd609c7343d243d3ee76b43acd65d6d1d467/dev_reals_config.tar.gz | [
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Dataset Card for EN-SLAM (Implicit Event-RGBD Neural SLAM, CVPR24)
Dataset Description
This repository contains the dataset for the paper Implicit Event-RGBD Neural SLAM
, the first event-RGBD implicit neural SLAM framework that efficiently leverages event stream and RGBD to overcome challenges in extreme motion blur and lighting variation scenes. DEV-Indoors is obtained through Blender [6] and simulator [14], covering normal, motion blur, and dark scenes, providing 9 subsets with RGB images, depth maps, event streams, meshes, and trajectories. DEV-Reals is captured from real scenes, providing 8 challenging subsets under motion blur and lighting variation.
Dataset Sources
Update
- Release DEV-Indoors and DEV-Reals Dataset.
- Add Dataset Usage Instruction.
Usage
- Download and Extract (
export HF_ENDPOINT=https://hf-mirror.com
would be helpful if you are blocked)
huggingface-cli download --resume-download --local-dir-use-symlinks False delinqu/EN-SLAM-Dataset --local-dir EN-SLAM-Dataset
# Alternatively, you can use git clone the repo
git lfs install
git clone https://huggingface.co/datasets/delinqu/EN-SLAM-Dataset
If you only want to download a specific subset, use the following code:
from huggingface_hub import hf_hub_download
hf_hub_download(
repo_id="delinqu/EN-SLAM-Dataset",
filename="DEV-Indoors_config.tar.gz",
repo_type="dataset",
local_dir=".",
)
After downloading, you can use the following script to extract the tar.gz
, under the project root dir. The python script just simple unzip all the tar.gz files, feel free to customise:
python scripts/extract_dataset.py
The extracted Dataset will be in the following structure:
- Use a Dataloader
Please refer to datasets/dataset.py
for dataloader of DEVIndoors
and DEVReals
.
- Evaluation
To construct the evaluation subsets, we use frustum + occlusion + virtual cameras
that introduce extra virtual views to cover the occluded parts inside the region of interest in CoSLAM. The evaluation datasets are generated by randomly conducting 2000 poses and depths in Blender for each scene. We further manually add extra virtual views to cover all scenes. This process helps to evaluate the view synthesis and hole-filling capabilities of the algorithm. Please follow the neural_slam_eval with our groundtruth pointclouds and images.
Dataset Format
DEV-Indoors Dataset
- data structure
βββ groundtruth # evaluation metadata: pose, rgb, depth, mesh
β βββ apartment
β βββ room
β βββ workshop
βββ seq001_room_norm # normal sequence: event, rgb, depth, pose, camera_para
β βββ camera_para.txt
β βββ depth
β βββ depth_mm
β βββ event.zip
β βββ pose
β βββ rgb
β βββ timestamps.txt
β βββ seq001_room_norm.yaml
βββ seq002_room_blur # blur sequence: event, rgb, depth, pose, camera_para
β βββ depth
β βββ depth_mm
β βββ event.zip
β βββ pose
β βββ rgb
β βββ timestamps.txt
β βββ seq002_room_blur.yaml
βββ seq003_room_dark # dark sequence: event, rgb, depth, pose, camera_para
β βββ depth
β βββ depth_mm
β βββ event.zip
β βββ pose
β βββ rgb
β βββ timestamps.txt
β βββ seq003_room_dark.yaml
...
βββ seq009_workshop_dark
βββ depth
βββ depth_mm
βββ event.zip
βββ pose
βββ rgb
βββ timestamps.txt
βββ seq009_workshop_dark.yaml
- model: 3D model of the room, apartment, and workshop scene
model
βββ apartment
β βββ apartment.blend
β βββ hdri
β βββ room.blend
β βββ supp
β βββ Textures
βββ workshop
βββ hdri
βββ Textures
βββ workshop.blend
- scripts: scripts for data generation and visulization.
scripts
βββ camera_intrinsic.py # blender camera intrinsic generation tool.
βββ camera_pose.py # blender camera pose generation tool.
βββ npzs_to_frame.py # convert npz to frame.
βββ read_ev.py # read event data.
βββ viz_ev_frame.py # visualize event and frame.
DEV-Reals Dataset
DEV-Reals
βββ devreals.yaml # dataset metadata: camera parameters, cam2davis transformation matrix
|
βββ enslamdata1 # sequence: davis346, pose, rgbd
β βββ davis346
β βββ pose
β βββ rgbd
βββ enslamdata1.bag
βββ enslamdata2
β βββ davis346
β βββ pose
β βββ rgbd
βββ enslamdata2.bag
βββ enslamdata3
β βββ davis346
β βββ pose
β βββ rgbd
βββ enslamdata3.bag
...
βββ enslamdata8
β βββ davis346
β βββ pose
β βββ rgbd
βββ enslamdata8.bag
Citation
If you use this work or find it helpful, please consider citing:
@inproceedings{qu2023implicit,
title={Implicit Event-RGBD Neural SLAM},
author={Delin Qu, Chi Yan, Dong Wang, Jie Yin, Qizhi Chen, Yiting Zhang, Dan Xu and Bin Zhao and Xuelong Li},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year={2024}
}
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