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Object Instance Segmentation from RGB-D Data with Mask R-CNN

This is an extended implementation of matterport's Mask R-CNN implementation on Python 3, Keras, and TensorFlow that supports RGB-D input data. The model generates bounding boxes and segmentation masks for each instance of an object in the RGB(-D) image. It's based on Feature Pyramid Network (FPN) and a ResNet101 backbone.

In our experiments we showed that an additional depth input layer can improve the segmentation accuracy of Mask R-CNN by up to 31%.

Links: Presentation, Paper (unofficial CoRL 2018 submission)

RGB-D Instance Segmentation Sample

Training and evaluation scripts for the 2D-3D-S, ADE20K, Coco, NYU Depth V2, sceneNet and sceneNN datasets can be found under the instance_segmentation directory:

  • dataset.py: Hyperparameters config, interface to datasets

  • train.py: Training script

  • eval.ipynb: IPython notebook for the evaluation of the dataset & training

Please refer to Matterport's original implementation for the original documentation and more.