Dyson Robotics Laboratory , Imperial College London
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020
MoreFusion is an object-level reconstruction system that builds a map with known-shaped objects, exploiting volumetric reconstruction of detected objects in a real-time, incremental scene reconstruction senario. The key components are:
- Occupancy-based volumetric reconstruction of detected objects for model alignment in the later stage;
- Volumetric pose prediction that exploits volumetric reconstruction and CNN feature extraction from the image observation;
- Joint pose refinement of objects based on geometric consistency among objects and impenetrable space.
There're several options for installation:
- Python project only: Python framework for pose estimation (e.g., training, inference, refinement).
- ROS project for camera demonstration: ROS framework for object-level mapping with live cameras.
- ROS project for robotic demonstration: ROS framework for robotic demonstration with object-level mapping.
NOTE: We have developed this project on Ubuntu 16.04 (and ROS Kinetic, CUDA 10.1), so several code changes may be needed to adapt to other OS (and ROS, CUDA versions).
make install
mkdir -p ~/ros_morefusion/src
cd ~/ros_morefusion/src
git clone https://github.com/wkentaro/morefusion.git
cd morefusion
make install
cd ~/ros_morefusion
ln -s src/ros/*.sh .
./rosdep_install.sh
./catkin_build.robot_agent.sh
source .autoenv.zsh
robot-agent
: A computer for visual processing.robot-node
: A computer with real-time OS for Panda robot.
Same as above instruction: ROS project for camera demonstration.
mkdir -p ~/ros_morefusion/src
cd ~/ros_morefusion/src
git clone https://github.com/wkentaro/morefusion.git
cd ~/ros_morefusion
ln -s src/ros/*.sh .
./catkin_build.robot_node.sh
source devel/setup.bash
rosrun morefusion_panda create_udev_rules.sh
Pre-trained models are provided in the demos as following, so this process is optional to run the demos.
cd examples/ycb_video/instance_segm
./download_dataset.py
mpirun -n 4 python train_multi.py # 4-gpu training
./image_demo.py --model logs/XXX/XXX.npz
# baseline model (point-cloud-based)
cd examples/ycb_video/singleview_pcd
./download_dataset.py
./train.py --gpu 0 --centerize-pcd --pretrained-resnet18 # 1-gpu
mpirun -n 4 ./train.py --multi-node --centerize-pcd --pretrained-resnet18 # 4-gpu
# volumetric prediction model (3D-CNN-based)
cd examples/ycb_video/singleview_3d
./download_dataset.py
./train.py --gpu 0 --pretrained-resnet18 --with-occupancy # 1-gpu
mpirun -n 4 ./train.py --multi-node --pretrained-resnet18 --with-occupancy # 4-gpu
mpirun -n 4 ./train.py --multi-node --pretrained-resnet18 # w/o occupancy
# inference
./download_pretrained_model.py # for downloading pretrained model
./demo.py logs/XXX/XXX.npz
./evaluate.py logs/XXX
cd examples/ycb_video/pose_refinement
./check_icp_vs_icc.py # press [s] to start
# using orb-slam2 for camera tracking
roslaunch morefusion_panda_ycb_video rs_rgbd.launch
roslaunch morefusion_panda_ycb_video rviz_static.desk.launch
roslaunch morefusion_panda_ycb_video setup_static.desk.launch
# using robotic kinematics for camera tracking
roslaunch morefusion_panda_ycb_video rs_rgbd.robot.launch
roslaunch morefusion_panda_ycb_video rviz_static.robot.launch
roslaunch morefusion_panda_ycb_video setup_static.robot.launch
roslaunch morefusion_panda_ycb_video rs_rgbd.launch
roslaunch morefusion_panda_ycb_video rviz_dynamic.desk.launch
roslaunch morefusion_panda_ycb_video setup_dynamic.desk.launch
roslaunch morefusion_panda_ycb_video rs_rgbd.robot.launch
roslaunch morefusion_panda_ycb_video rviz_dynamic.robot.launch
roslaunch morefusion_panda_ycb_video setup_dynamic.robot.launch
robot-agent $ sudo ntpdate 0.uk.pool.ntp.org # for time synchronization
robot-node $ sudo ntpdate 0.uk.pool.ntp.org # for time synchronization
robot-node $ roscore
robot-agent $ roslaunch morefusion_panda panda.launch
robot-node $ roslaunch morefusion_panda_ycb_video rs_rgbd.robot.launch
robot-node $ roslaunch morefusion_panda_ycb_video rviz_static.launch
robot-node $ roslaunch morefusion_panda_ycb_video setup_static.robot.launch TARGET:=2
robot-node $ rosrun morefusion_panda_ycb_video robot_demo_node.py
>>> ri.run()
Figure 4. Targetted Object Pick-and-Place. (a) Scanning the Scene; (b) Removing Distractor Objects; (c) Picking Target Object.
If you find MoreFusion useful, please consider citing the paper as:
@inproceedings{Wada:etal:CVPR2020,
title={{MoreFusion}: Multi-object Reasoning for {6D} Pose Estimation from Volumetric Fusion},
author={Kentaro Wada and Edgar Sucar and Stephen James and Daniel Lenton and Andrew J. Davison},
booktitle={Proceedings of the {IEEE} Conference on Computer Vision and Pattern Recognition ({CVPR})},
year={2020},
}