ElasticFusion-Dockerfile
ElasticFusion-Dockerfile copied to clipboard
Dockerfile for the use of [ElasticFusion](https://github.com/mp3guy/ElasticFusion)
ElasticFusion Dockerfile
Dockerfile for use of ElasticFusion with RealSense
Requirements
- Docker
- from: https://docs.docker.com/get-docker/
- NVIDIA Container Toolkit
- from: https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html
My environment (ref.)
- Ubuntu 20.04
- CUDA 11.2 (host)
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 460.32.03 Driver Version: 460.32.03 CUDA Version: 11.2 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 GeForce GTX 1080 On | 00000000:01:00.0 On | N/A |
| 46% 52C P2 67W / 180W | 3900MiB / 8116MiB | 26% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
Docker build and run
$ docker build -t <image_name> ./docker
$ xhost local:
$ ./opendocker.sh <image_name>
$ xhost -local:
Run with RealSense
I tested only with RealSense D435.
$ ElasticFusion
# data is saved as `/opt/ElasticFusion/GUIlive.ply`
Run with sample data
$ wget http://www.doc.ic.ac.uk/~sleutene/datasets/elasticfusion/dyson_lab.klg -P ./workspace
# in container
$ ElasticFusion -l dyson_lab.klg
Visualize result
$ pipenv sync
$ pipenv shell
$ python visualize.py --ply <path/to/.ply>