Yiran Wang

TL;DR: The first Mobile AI challenge as discussed by the authors was introduced to develop an end-to-end deep learning-based depth estimation solutions that can demonstrate a nearly real-time performance on smartphones and IoT platforms, where participants were provided with a new large-scale dataset containing RGB-depth image pairs obtained with a dedicated stereo ZED camera producing high-resolution depth maps for objects located at up to 50 meters.

TL;DR: In this article, knowledge distillation is used to transfer the knowledge and representation ability of a stronger teacher network to a light-weight student network, which helps increase the fidelity of the output depth map and maintain fast inference speed.

TL;DR: The first Mobile AI challenge as discussed by the authors was introduced to develop an end-to-end deep learning-based depth estimation solutions that can demonstrate a nearly real-time performance on smartphones and IoT platforms.

TL;DR: In this paper , the authors used a large-scale RGB-to-depth dataset that was collected with the ZED stereo camera capable to generate depth maps for objects located at up to 50 meters.

TL;DR: Experiments on synthetic and real-world datasets show that SymmNeRF synthesizes novel views with more details regardless of the pose transformation, and demonstrates good generalization when applied to unseen objects.