Research links

Data Processing:

Deep Learning:

Depth-aware CNN for RGB-D Segmentation (2018):

• Paper

Learning Rich Features from RGB-D Images forObject Detection and Segmentation (2014):

Comment: Introducing (?) RGB-D and HHA for semseg

• Paper

Depth Not Needed - An Evaluation of RGB-D Feature Encodings for Off-Road Scene Understanding by Convolutional Neural Network ():

Comment: Discussing the 'efficiency' of depth data (and different types of depth data), Paper conclusion:

Our results show that the additional information contained in stereo disparity can provide marginal improvement to CNN classification performance compared to standard RGB images in this particularly challenging problem of off-road scene understanding. However, careful consideration of how this information is encoded is necessary, and in our case the combination of RGB data with each pixel’s height above ground plane was shown to give the best results. The performance gains demonstrated are small, however this is mostly due to the efficacy of the existing CNN architecture at classification from conventional colour images (RGB), with results so good to begin with that it becomes ever harder to gain extra performance. This is counter to results such as those from Gupta et al [8] and Pavel et al [9], who claim that RGBD data provides significant improvements in CNN classification performance over RGB imagery. This demonstrates that while depth information may improve CNN performance in an indoor object detection task, it is of limited utility in the more challenging off-road environment, where poorly defined class boundaries and inconsistent object shape can hamper classification accuracy.

• Paper