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[ECCV'22] A Visual Navigation Perspective for Category-Level Object Pose Estimation

You can use anaconda and create an anaconda environment:

conda env create -f environment.yml
conda activate visual_nav

We set neural_object_fitting as our image generator for NOCS, their checkpoints and datasets could be downloaded following:

sh prepare_datasets.sh

Download the pretrained models here and put them into ./pretrained_model/.

Run the following command to train on specific category (can / bottle / bowl / mug / laptop / camera)

python main.py --dataset [category] --name [running name]

The saved models and evaluation results could be check at './results/'.

To visualize the training process, you can run:

# use wandb to visualize the training loss and states
python main.py --dataset [category] --log True --log_interval 50

Then open the wandb link to monitor the training process.

There are several settings you can change by adding arguments below:

Arguments	What it will trigger	Default
--batch_size	The batch size of input	50
--lr	The learning rate for training	0.00003
--pretrain	Continue to train with pretrained model	None
--save_interval	save model interval	1000
--episode_nums	maximum episodes number	50000

To evaluate on synthetic dataset based on the pretrained model, run the following command:

python main.py --dataset [category] --eval 1 --pretrain [path] --gd_optimize True

The evaluation results will be reported with plot.

To evaluate on real dataset, run the following command:

python main.py --dataset [category] --eval 2 --pretrain [path] --gd_optimize True

To calculate the score:

python nocs/eval.py --dataset [category]

The evaluation results of specific category will be reported.