I unfortunately did not get the approval to release the context-aware frame interpolation, but one of my older works is open source for research purposes: https://github.com/sniklaus/pytorch-sepconv
1. I am not sure about the scale of the individual loss functions anymore, my apologies. I determined the combination of the two losses via a simple grid search and seeing what works best (plus / minus a magnitude did not make that much of a difference).
2. The points are just splatted to an image plane, more advanced point cloud rendering techniques would be better though. There is no video frame interpolation, each individual frame in the output video is a rendering of the point cloud from a different camera perspective.
3. I am not sure about multi-view stereo, COLMAP still seems like the state of the art for that. But neural networks definitely outperform classic techniques for single image depth estimation.
4. Common architectures just did not do as well as I was hoping for so I tried about 1500 model architectures. I started with an architecture that intuitively seemed right and then gradually explored / refined alterations of it. It ultimately was a lot of trial and error.
1. I am not sure about the scale of the individual loss functions anymore, my apologies. I determined the combination of the two losses via a simple grid search and seeing what works best (plus / minus a magnitude did not make that much of a difference).
2. The points are just splatted to an image plane, more advanced point cloud rendering techniques would be better though. There is no video frame interpolation, each individual frame in the output video is a rendering of the point cloud from a different camera perspective.
3. I am not sure about multi-view stereo, COLMAP still seems like the state of the art for that. But neural networks definitely outperform classic techniques for single image depth estimation.
4. Common architectures just did not do as well as I was hoping for so I tried about 1500 model architectures. I started with an architecture that intuitively seemed right and then gradually explored / refined alterations of it. It ultimately was a lot of trial and error.