Geometry-Based Next Frame Prediction from Monocular Video
This addresses the problem of predicting future frames for applications like autonomous driving, but it is incremental as it builds on existing geometry-based methods by incorporating depth prediction from video rather than single images.
The paper tackles next frame prediction from monocular video by using a recurrent convolutional neural network to predict depth from video sequences, which is then combined with camera trajectory to compute the next frame, achieving visually and numerically superior results on the KITTI dataset with improved depth accuracy as more frames are used.
We consider the problem of next frame prediction from video input. A recurrent convolutional neural network is trained to predict depth from monocular video input, which, along with the current video image and the camera trajectory, can then be used to compute the next frame. Unlike prior next-frame prediction approaches, we take advantage of the scene geometry and use the predicted depth for generating the next frame prediction. Our approach can produce rich next frame predictions which include depth information attached to each pixel. Another novel aspect of our approach is that it predicts depth from a sequence of images (e.g. in a video), rather than from a single still image. We evaluate the proposed approach on the KITTI dataset, a standard dataset for benchmarking tasks relevant to autonomous driving. The proposed method produces results which are visually and numerically superior to existing methods that directly predict the next frame. We show that the accuracy of depth prediction improves as more prior frames are considered.