FSNet: Redesign Self-Supervised MonoDepth for Full-Scale Depth Prediction for Autonomous Driving

• URL: http://arxiv.org/abs/2304.10719v1
• Date: Fri, 21 Apr 2023 03:17:04 GMT
• Title: FSNet: Redesign Self-Supervised MonoDepth for Full-Scale Depth Prediction for Autonomous Driving
• Authors: Yuxuan Liu, Zhenhua Xu, Huaiyang Huang, Lujia Wang, Ming Liu
• Abstract summary: This study proposes a comprehensive self-supervised framework for accurate scale-aware depth prediction on autonomous driving scenes. In particular, we introduce a Full-Scale depth prediction network named FSNet. With FSNet, robots and vehicles with only one well-calibrated camera can collect sequences of training image frames and camera poses, and infer accurate 3D depths of the environment without extra labeling work or 3D data.
• Score: 18.02943016671203
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Predicting accurate depth with monocular images is important for low-cost robotic applications and autonomous driving. This study proposes a comprehensive self-supervised framework for accurate scale-aware depth prediction on autonomous driving scenes utilizing inter-frame poses obtained from inertial measurements. In particular, we introduce a Full-Scale depth prediction network named FSNet. FSNet contains four important improvements over existing self-supervised models: (1) a multichannel output representation for stable training of depth prediction in driving scenarios, (2) an optical-flow-based mask designed for dynamic object removal, (3) a self-distillation training strategy to augment the training process, and (4) an optimization-based post-processing algorithm in test time, fusing the results from visual odometry. With this framework, robots and vehicles with only one well-calibrated camera can collect sequences of training image frames and camera poses, and infer accurate 3D depths of the environment without extra labeling work or 3D data. Extensive experiments on the KITTI dataset, KITTI-360 dataset and the nuScenes dataset demonstrate the potential of FSNet. More visualizations are presented in \url{https://sites.google.com/view/fsnet/home}

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