Dense Depth Posterior (DDP) from Single Image and Sparse Range

Understand

We present a deep learning system to infer the posterior distribution of a dense depth map associated with an image, by exploiting sparse range measurements, for instance from a lidar.

• While the lidar may provide a depth value for a small percentage of the pixels, we exploit regularities reflected in the training set to complete the map so as to have a probability over depth for each pixel in the image.
• We exploit a Conditional Prior Network, that allows associating a probability to each depth value given an image, and combine it with a likelihood term that uses the sparse measurements.
• Optionally we can also exploit the availability of stereo during training, but in any case only require a single image and a sparse point cloud at run-time.