A Supervised Approach to Predicting Noise in Depth Images.

Modern robotic systems are very complex and need to be tested in simulations with detailed sensor noise models to effectively verify robotic behavior. Depth imagery in particular comes with significant noise in the form of scene-dependent pixel-wise dropouts and distortions. Unfortunately, many depth camera simulations contain limited noise models, or can only support generating realistic depth images of simple scenes, which limits their usefulness in effectively testing perception algorithms. We propose a data driven approach to generate more realistic noise for complex simulated environments by using a convolutional neural network (CNN) to predict which pixels of a simulated noise-free depth image will not have returns (no-depth-return pixels, or NDP). We choose to focus on NDP here, as these dropouts are the most common and dramatic form of depth image noise. To train this network, we use reconstructed real-world scenes from the Label Fusion dataset to provide ground truth depth for each noisy depth image used to scan the scene. We use the resulting noise-free and noisy depth image pairs as labeled examples and train the network to predict which pixels of the noise-free image will be NDP. When used to post-process a simulation of a depth sensor, this system produces realistic depth images, even in cluttered scenes. To demonstrate that our approach successfully closes the reality gap for depth imagery, we show that the popular ICP algorithm for object pose estimation fails more realistically on our CNN-corrupted simulated depth images than on uncorrupted depth images and unsupervised domain adaptation baselines.