MSDPN: Monocular Depth Prediction with Partial Laser Observation using Multi-stage Neural Networks

• URL: http://arxiv.org/abs/2008.01405v1
• Date: Tue, 4 Aug 2020 08:27:40 GMT
• Title: MSDPN: Monocular Depth Prediction with Partial Laser Observation using Multi-stage Neural Networks
• Authors: Hyungtae Lim, Hyeonjae Gil and Hyun Myung
• Abstract summary: We propose a deep-learning-based multi-stage network architecture called Multi-Stage Depth Prediction Network (MSDPN) MSDPN is proposed to predict a dense depth map using a 2D LiDAR and a monocular camera. As verified experimentally, our network yields promising performance against state-of-the-art methods.
• Score: 1.1602089225841632
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this study, a deep-learning-based multi-stage network architecture called Multi-Stage Depth Prediction Network (MSDPN) is proposed to predict a dense depth map using a 2D LiDAR and a monocular camera. Our proposed network consists of a multi-stage encoder-decoder architecture and Cross Stage Feature Aggregation (CSFA). The proposed multi-stage encoder-decoder architecture alleviates the partial observation problem caused by the characteristics of a 2D LiDAR, and CSFA prevents the multi-stage network from diluting the features and allows the network to learn the inter-spatial relationship between features better. Previous works use sub-sampled data from the ground truth as an input rather than actual 2D LiDAR data. In contrast, our approach trains the model and conducts experiments with a physically-collected 2D LiDAR dataset. To this end, we acquired our own dataset called KAIST RGBD-scan dataset and validated the effectiveness and the robustness of MSDPN under realistic conditions. As verified experimentally, our network yields promising performance against state-of-the-art methods. Additionally, we analyzed the performance of different input methods and confirmed that the reference depth map is robust in untrained scenarios.

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