Sensor Simulation for Monocular Depth Estimation using Deep Neural Networks

Nadar, Christon R.; Kunert, Christian; Schwandt, Tobias; Broll, Wolfgang

doi:10.1109/cw52790.2021.00010

Cited by 1 publication

(2 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Later, they introduce adaptive bins to estimate the depth map more precisely [2]. In our previous work [21], we employed the model architecture from Alhashim and Wonka [1] in order to simulate the behavior of a Structure Sensor. Hereby, a transfer learning was used in conjunction with various data processing techniques.…”

Section: Architecturesmentioning

confidence: 99%

See 1 more Smart Citation

Neural network adaption for depth sensor replication

et al. 2022

Self Cite

View full text Add to dashboard Cite

In recent years, various depth sensors that are small enough to be used with mobile hardware have been introduced. They provide important information for use cases like 3D reconstruction or in the context of augmented reality where tracking and camera data alone would be insufficient. However, depth sensors may not always be available due to hardware limitations or when simulating augmented reality applications for prototyping purposes. In these cases, different approaches like stereo matching or depth estimation using neural networks may provide a viable alternative. In this paper, we therefore explore the imitation of depth sensors using deep neural networks. For this, we use a state-of-the-art network for depth estimation and adapt it in order to mimic a Structure Sensor as well as an iPad LiDAR sensor. We evaluate the network which was pre-trained on NYU V2 directly as well as several variations where transfer learning is applied in order to adapt the network to different depth sensors while using various data preprocessing and augmentation techniques. We show that a transfer learning approach together with appropriate data processing can enable an accurate modeling of the respective depth sensors.

show abstract

Section: Architecturesmentioning

confidence: 99%

“…The evaluation results listed in Table 2 are obtained using the accuracy and error metrics shown here. We rely on established metrics that were used in prior work regarding depth estimation evaluation [1,5,21].…”

Section: Metricsmentioning

confidence: 99%