Home

Awesome

Augmented Autoencoders

Implicit 3D Orientation Learning for 6D Object Detection from RGB Images

Martin Sundermeyer, Zoltan-Csaba Marton, Maximilian Durner, Manuel Brucker, Rudolph Triebel
Best Paper Award, ECCV 2018.

paper, supplement, oral

Citation

If you find Augmented Autoencoders useful for your research, please consider citing:

@InProceedings{Sundermeyer_2018_ECCV,
author = {Sundermeyer, Martin and Marton, Zoltan-Csaba and Durner, Maximilian and Brucker, Manuel and Triebel, Rudolph},
title = {Implicit 3D Orientation Learning for 6D Object Detection from RGB Images},
booktitle = {The European Conference on Computer Vision (ECCV)},
month = {September},
year = {2018}
}

Multi-path Learning for Object Pose Estimation Across Domains

Martin Sundermeyer, Maximilian Durner, En Yen Puang, Zoltan-Csaba Marton, Narunas Vaskevicius, Kai O. Arras, Rudolph Triebel
CVPR 2020
The code of this work can be found here

Overview

We propose a real-time RGB-based pipeline for object detection and 6D pose estimation. Our novel 3D orientation estimation is based on a variant of the Denoising Autoencoder that is trained on simulated views of a 3D model using Domain Randomization. This so-called Augmented Autoencoder has several advantages over existing methods: It does not require real, pose-annotated training data, generalizes to various test sensors and inherently handles object and view symmetries.

<p align="center"> <img src='docs/pipeline_with_scene_vertical_ext.jpeg' width='600'> <p>

1.) Train the Augmented Autoencoder(s) using only a 3D model to predict 3D Object Orientations from RGB image crops
2.) For full RGB-based 6D pose estimation, also train a 2D Object Detector (e.g. https://github.com/fizyr/keras-retinanet)
3.) Optionally, use our standard depth-based ICP to refine the 6D Pose

Requirements: Hardware

For Training

Nvidia GPU with >4GB memory (or adjust the batch size)
RAM >8GB
Duration depending on Configuration and Hardware: ~3h per Object

Requirements: Software

Linux, Python 2.7 / Python 3

GLFW for OpenGL:

sudo apt-get install libglfw3-dev libglfw3

Assimp:

sudo apt-get install libassimp-dev

Tensorflow >= 1.6
OpenCV >= 3.1

pip install --pre --upgrade PyOpenGL PyOpenGL_accelerate
pip install cython
pip install cyglfw3
pip install pyassimp==3.3
pip install imgaug
pip install progressbar

Headless Rendering

Please note that we use the GLFW context as default which does not support headless rendering. To allow for both, onscreen rendering & headless rendering on a remote server, set the context to EGL:

export PYOPENGL_PLATFORM='egl'

In order to make the EGL context work, you might need to change PyOpenGL like here

Support for Tensorflow 2.6 / Python 3

The code now also supports TF 2.6 with python 3. Instead of the pip installs above, you can also use the provided conda environment.

conda env create -f aae_py37_tf26.yml

In the activated environment proceed with the preparatory steps.

Preparatory Steps

1. Pip installation

pip install .

2. Set Workspace path, consider to put this into your bash profile, will always be required

export AE_WORKSPACE_PATH=/path/to/autoencoder_ws

3. Create Workspace, Init Workspace (if installed locally, make sure .local/bin/ is in your PATH)

mkdir $AE_WORKSPACE_PATH
cd $AE_WORKSPACE_PATH
ae_init_workspace

Train an Augmented Autoencoder

1. Create the training config file. Insert the paths to your 3D model and background images.

mkdir $AE_WORKSPACE_PATH/cfg/exp_group
cp $AE_WORKSPACE_PATH/cfg/train_template.cfg $AE_WORKSPACE_PATH/cfg/exp_group/my_autoencoder.cfg
gedit $AE_WORKSPACE_PATH/cfg/exp_group/my_autoencoder.cfg

2. Generate and check training data. The object views should be strongly augmented but identifiable.

(Press ESC to close the window.)

ae_train exp_group/my_autoencoder -d

This command does not start training and should be run on a PC with a display connected.

Output:

3. Train the model (See the Headless Rendering section if you want to train directly on a server without display)

ae_train exp_group/my_autoencoder

$AE_WORKSPACE_PATH/experiments/exp_group/my_autoencoder/train_figures/training_images_29999.png

Middle part should show reconstructions of the input object (if all black, set higher bootstrap_ratio / auxilliary_mask in training config)

4. Create the embedding

ae_embed exp_group/my_autoencoder

Testing

Augmented Autoencoder only

have a look at /auto_pose/test/

Feed one or more object crops from disk into AAE and predict 3D Orientation

python aae_image.py exp_group/my_autoencoder -f /path/to/image/file/or/folder

The same with a webcam input stream

python aae_webcam.py exp_group/my_autoencoder

Multi-object RGB-based 6D Object Detection from a Webcam stream

Option 1: Train a RetinaNet Model from https://github.com/fizyr/keras-retinanet

adapt $AE_WORKSPACE_PATH/eval_cfg/aae_retina_webcam.cfg

python auto_pose/test/aae_retina_webcam_pose.py -test_config aae_retina_webcam.cfg -vis

Option 2: Using the Google Detection API with Fixes

Train a 2D detector following https://github.com/naisy/train_ssd_mobilenet
adapt /auto_pose/test/googledet_utils/googledet_config.yml

python auto_pose/test/aae_googledet_webcam_multi.py exp_group/my_autoencoder exp_group/my_autoencoder2 exp_group/my_autoencoder3

Evaluate a model

Reproducing and visualizing BOP challenge results

Here are AAE models trained the BOP datasets with codebooks of all 108 objects:

Download

Extract it to $AE_WORKSPACE_PATH/experiments

Also get precomputed MaskRCNN predictions for all BOP datasets:

Download

Open the bop20 evaluation configs, e.g. auto_pose/ae/cfg_m3vision/m3_config_lmo.cfg, and point the path_to_masks parameter to the downloaded maskrcnn predictions.

You can visualize (-vis option) and reproduce BOP results by running:

python auto_pose/m3_interface/compute_bop_results_m3.py auto_pose/ae/cfg_m3vision/m3_config_lmo.cfg 
                                                     --eval_name test 
                                                     --dataset_name=lmo 
                                                     --datasets_path=/path/to/bop/datasets 
                                                     --result_folder /folder/to/results 
                                                     -vis

Note: You will need the bop_toolkit. I created a package bop_toolkit_lib from it, but you can also just add the required files to sys.path()

Original paper evaluation with T-LESS v1

For the evaluation you will also need https://github.com/thodan/sixd_toolkit + our extensions, see sixd_toolkit_extension/help.txt

Create the evaluation config file

mkdir $AE_WORKSPACE_PATH/cfg_eval/eval_group
cp $AE_WORKSPACE_PATH/cfg_eval/eval_template.cfg $AE_WORKSPACE_PATH/cfg_eval/eval_group/eval_my_autoencoder.cfg
gedit $AE_WORKSPACE_PATH/cfg_eval/eval_group/eval_my_autoencoder.cfg

Evaluate and visualize 6D pose estimation of AAE with ground truth bounding boxes

Set estimate_bbs=False in the evaluation config

ae_eval exp_group/my_autoencoder name_of_evaluation --eval_cfg eval_group/eval_my_autoencoder.cfg
e.g.
ae_eval tless_nobn/obj5 eval_name --eval_cfg tless/5.cfg

Evaluate 6D Object Detection with a 2D Object Detector

Set estimate_bbs=True in the evaluation config

Generate a training dataset for T-Less using detection_utils/generate_sixd_train.py

python detection_utils/generate_sixd_train.py

Train https://github.com/fizyr/keras-retinanet or https://github.com/balancap/SSD-Tensorflow

ae_eval exp_group/my_autoencoder name_of_evaluation --eval_cfg eval_group/eval_my_autoencoder.cfg
e.g.
ae_eval tless_nobn/obj5 eval_name --eval_cfg tless/5.cfg

Config file parameters

[Paths]
# Path to the model file. All formats supported by assimp should work. Tested with ply files.
MODEL_PATH: /path/to/my_3d_model.ply
# Path to some background image folder. Should contain a * as a placeholder for the image name.
BACKGROUND_IMAGES_GLOB: /path/to/VOCdevkit/VOC2012/JPEGImages/*.jpg

[Dataset]
#cad or reconst (with texture)
MODEL: reconst
# Height of the AE input layer
H: 128
# Width of the AE input layer
W: 128
# Channels of the AE input layer (default BGR)
C: 3
# Distance from Camera to the object in mm for synthetic training images
RADIUS: 700
# Dimensions of the renderered image, it will be cropped and rescaled to H, W later.
RENDER_DIMS: (720, 540)
# Camera matrix used for rendering and optionally for estimating depth from RGB
K: [1075.65, 0, 720/2, 0, 1073.90, 540/2, 0, 0, 1]
# Vertex scale. Vertices need to be scaled to mm
VERTEX_SCALE: 1
# Antialiasing factor used for rendering
ANTIALIASING: 8
# Padding rendered object images and potentially bounding box detections 
PAD_FACTOR: 1.2
# Near plane
CLIP_NEAR: 10
# Far plane
CLIP_FAR: 10000
# Number of training images rendered uniformly at random from SO(3)
NOOF_TRAINING_IMGS: 10000
# Number of background images that simulate clutter
NOOF_BG_IMGS: 10000

[Augmentation]
# Using real object masks for occlusion (not really necessary)
REALISTIC_OCCLUSION: False
# Maximum relative translational offset of input views, sampled uniformly  
MAX_REL_OFFSET: 0.20
# Random augmentations at random strengths from imgaug library
CODE: Sequential([
    #Sometimes(0.5, PerspectiveTransform(0.05)),
    #Sometimes(0.5, CropAndPad(percent=(-0.05, 0.1))),
    Sometimes(0.5, Affine(scale=(1.0, 1.2))),
    Sometimes(0.5, CoarseDropout( p=0.2, size_percent=0.05) ),
    Sometimes(0.5, GaussianBlur(1.2*np.random.rand())),
    Sometimes(0.5, Add((-25, 25), per_channel=0.3)),
    Sometimes(0.3, Invert(0.2, per_channel=True)),
    Sometimes(0.5, Multiply((0.6, 1.4), per_channel=0.5)),
    Sometimes(0.5, Multiply((0.6, 1.4))),
    Sometimes(0.5, ContrastNormalization((0.5, 2.2), per_channel=0.3))
    ], random_order=False)

[Embedding]
# for every rotation save rendered bounding box diagonal for projective distance estimation
EMBED_BB: True
# minimum number of equidistant views rendered from a view-sphere
MIN_N_VIEWS: 2562
# for each view generate a number of in-plane rotations to cover full SO(3)
NUM_CYCLO: 36

[Network]
# additionally reconstruct segmentation mask, helps when AAE decodes pure blackness
AUXILIARY_MASK: False
# Variational Autoencoder, factor in front of KL-Divergence loss
VARIATIONAL: 0
# Reconstruction error metric
LOSS: L2
# Only evaluate 1/BOOTSTRAP_RATIO of the pixels with highest errors, produces sharper edges
BOOTSTRAP_RATIO: 4
# regularize norm of latent variables
NORM_REGULARIZE: 0
# size of the latent space
LATENT_SPACE_SIZE: 128
# number of filters in every Conv layer (decoder mirrored)
NUM_FILTER: [128, 256, 512, 512]
# stride for encoder layers, nearest neighbor upsampling for decoder layers
STRIDES: [2, 2, 2, 2]
# filter size encoder
KERNEL_SIZE_ENCODER: 5
# filter size decoder
KERNEL_SIZE_DECODER: 5


[Training]
OPTIMIZER: Adam
NUM_ITER: 30000
BATCH_SIZE: 64
LEARNING_RATE: 1e-4
SAVE_INTERVAL: 5000

[Queue]
# number of threads for producing augmented training data (online)
NUM_THREADS: 10
# preprocessing queue size in number of batches
QUEUE_SIZE: 50