Papers › FreDSNet: Joint Monocular Depth and Semantic Segmentation with Fast Fourier Convolutions
FreDSNet: Joint Monocular Depth and Semantic Segmentation with Fast Fourier Convolutions
Bruno Berenguel-Baeta, Jesus Bermudez-Cameo, Jose J. Guerrero
In this work we present FreDSNet, a deep learning solution which obtains semantic 3D understanding of indoor environments from single panoramas. Omnidirectional images reveal task-specific advantages when addressing scene understanding problems due to the 360-degree contextual information about the entire environment they provide. However, the inherent characteristics of the omnidirectional images add additional problems to obtain an accurate detection and segmentation of objects or a good depth estimation. To overcome these problems, we exploit convolutions in the frequential domain obtaining a wider receptive field in each convolutional layer. These convolutions allow to leverage the whole context information from omnidirectional images. FreDSNet is the first network that jointly provides monocular depth estimation and semantic segmentation from a single panoramic image exploiting fast Fourier convolutions. Our experiments show that FreDSNet has similar performance as specific state of the art methods for semantic segmentation and depth estimation. FreDSNet code is publicly available in https://github.com/Sbrunoberenguel/FreDSNet
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Depth Estimation | Stanford2D3D Panoramic | FreDSNet | RMSE | 0.2727 | #2 of 18 | Archive leaderboard | report |
| Depth Estimation | Stanford2D3D Panoramic | FreDSNet | absolute relative error | 0.0952 | #2 of 18 | Archive leaderboard | report |
| Semantic Segmentation | Stanford2D3D Panoramic | FreDSNet | mAcc | 63.1 | #18 of 25 | Archive leaderboard | report |
| Semantic Segmentation | Stanford2D3D Panoramic | FreDSNet | mIoU | 46.1% | #18 of 25 | Archive leaderboard | report |
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