The dataset used is a portion of the dataset from the DIODE paper (...) the dataset provided consists of 2k RGB images of indoor scenes, and as many files for the reactive mask and depth_map, for a total of 10GB. Indoor scenes are much easier than outdoor scenes as the range of values is smaller.

In RGBD images, the red colour indicates that that pixel has a high value, while the blue colour indicates that that pixel has a low value.

We performed a clipping of the image values

and decribe the min/max/mean value distribution Analysis

The following operations (Augmentation) were performed in combination, resulting in a 30% increase in dataset size:

• Horizontal flip
• Inversion of bands
• Saturation
• Gaussian filtering 

We define all parameters in Configuration.yaml

• Encoder decoder with DenseNet121 like encoder

  

• Encoder decoder with DenseNet121 like encoder with skipp connection

  

• Plain Models

  

• Plain Models with VGG16

  

• Plain Models with VGG16 with leakyRelu

  

• Complexity of the task in relation to poor data availability
• Insufficient hardware resources for training more massive models
• Difficulties in reconstructing the depth map
• Improvements through transfer learning in both Encoder-Decoder and plain architecture contexts