TexPose

Official repository for "TexPose: Neural Texture Learning for Self-Supervised 6D Object Pose Estimation", CVPR 2023.

TL;DR

We re-formulate self-supervised pose estimation as two sub-optimization problems on texture learning and pose learning. With this re-formulation, we can do effective self-training for object pose estimator without any supervision signals like depth or deep pose refiner requried in previous works.

Prerequisites

Set up the environment with cond

conda env create --file requirements.yaml python=3.8
conda activate texpose

Download PyTorch3D following the instruction here.

Data Preparation

We use Duck class (ID 9) from LineMOD as an example here:

Download dataset and predicted pose labels after synthetic pretraining

Download our pre-processed dataset from here

Download pre-trained weights

Download the pre-trained weights (pre-trained geometric branch and texture learner) from here

Compute box for ray sampling

You can skip this step if you download our pre-processed dataset.

python3 compute_box.py --pred_loop init_calib --object_id 9 --generate_pred \
--save_predbox --target_folder dataset/lm/lm_test_all/test/000009/

Generate surfel information and synthetic image

You can skip this step if you download our pre-processed dataset.

python3 compute_surfelinfo.py --name='' \
--model=nerf_adapt_st_gan --yaml=nerf_lm_adapt_gan \
--data.pose_source=predicted --data.pose_loop=init_calib \
--gan= --loss_weight.feat=  \
--batch_size=1 \
--data.object=duck  \
--render.geo_save_dir=dataset/lm/lm_test_all/test/000009/

Running the code

We use Duck class (ID 9) from LineMOD as an example here:

Synthesize novel views

python3 evaluate.py --model=nerf_adapt_st_gan --yaml=nerf_lm_adapt_gan --batch_size=1  
--data.preload=false --data.object=duck --data.scene=scene_syn2real_layer --name=test_run3 \
--data.image_size=[480,640] --resume --syn2real --render.save_path=PATH/YOU/WANT/TO/SAVE/

Train texture learner

python3 train.py --model=nerf_adapt_st_gan --yaml=nerf_lm_adapt_gan --resume_pretrain \
--data.pose_source=predicted --data.preload=true  --group Duck --data.object=duck \
--data.scene=scene_all --name=test_run3

Supervise pose estimator

Now you can use the synthesized data to supervise the pose estimator. All synthesized data is organized in BOP format for ease of adapting to variant pose estimators. We use improved GDR-Net from Self6D++.

Citation

If you find our work useful, please consider citing us:

@inproceedings{chen2023texpose,
title = {TexPose: Neural Texture Learning for Self-Supervised 6D Object Pose Estimation},
author = {Hanzhi Chen and
            Fabian Manhardt and
            Nassir Navab and
            Benjamin Busam},
journal={IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2023}
}

Acknowledgement

Our implementation is based on BARF and follows their code structure. Thanks for their great contribution!

HanzhiC / TexPose