What are good data augmentation techniques which can work with 3d data specifically depth being third dimension. Also, any pointers on tools around them would be of great help.
Are you talking about a depth channel on an image plane? It also largely depend on the problem but here is a few tricks that helped me for my problems (object tracking).<p>- Generating synthetic data is powerful if you have the depth modality as it is easy to render. Also the real/synthetic domain gap is narrow compared to RGB. I consider it as data augmentation: you usually do many renders from a single 3D model.<p>- If you can somehow normalize the offset (e.g. compute normals) that can help. In my case I could offset the center of the object as 0 depth and it greatly help the network to converge.<p>- Classic augmentations like gaussian noise, gaussian blur and also downsampling the depth helps (apply these randomly).<p>As for tooling, I just use numpy/pytorch for most operations and OpenGL for renders.