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Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation on Python3, Tensorflow, and Keras
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README.md

Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation

This is a keras and tensorflow implementation of Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation, a CVPR 2019 oral paper.

NOCS Teaser

The repository includes:

  • Source code of NOCS.
  • Training code
  • Detection and evaluation code
  • Pre-trained weights

Datasets

NOTE: You are required to cite our paper if you use the dataset. The data is only for non-commercial use. Please reach out to us for other use cases.

Requirements

  • Python 3.5
  • Tensorflow 1.3.0
  • Keras 2.1.5
  • cPickle

Training

# Train a new model from pretrained COCO weight
python3 train.py

Detection and Evaluation

# Detect using a checkpoint
python3 detect_eval.py --mode detect --ckpt_path=/ckpts/ckpt 

# Evaluate a checkpoint
python3 detect_eval.py --mode eval --ckpt_path=/ckpts/ckpt 

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