SliceNet: Deep Dense Depth Estimation From a Single Indoor Panorama Using a Slice-Based Representation
We introduce a novel deep neural network to estimate a depth map from a single monocular indoor panorama. The network directly works on the equirectangular projection, exploiting the properties of indoor 360 images. Starting from the fact that gravity plays an important role in the design and construction of man-made indoor scenes, we propose a compact representation of the scene into vertical slices of the sphere, and we exploit long- and short-term relationships among slices to recover the equirectangular depth map. Our design makes it possible to maintain high-resolution information in the extracted features even with a deep network. The experimental results demonstrate that our method outperforms current state-of-the-art solutions in prediction accuracy, particularly for real-world data.
Code (0)
등록된 구현이 없습니다.
Tasks
Depth EstimationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Recurrent Neural Network for Learning DenseDepth and Ego-Motion from Video
Learning-based, single-view depth estimation often generalizes poorly to unseen datasets. While learning-based, two-frame depth estimation solves this problem to some extent by learning to match features across frames, i…
3D ReconstructionDepth EstimationMotion EstimationPano Popups: Indoor 3D Reconstruction with a Plane-Aware Network
In this work we present a method to train a plane-aware convolutional neural network for dense depth and surface normal estimation as well as plane boundaries from a single indoor $360^\circ$ image. Using our proposed lo…
3D ReconstructionDepth EstimationSurface Normal EstimationDnD: Dense Depth Estimation in Crowded Dynamic Indoor Scenes
We present a novel approach for estimating depth from a monocular camera as it moves through complex and crowded indoor environments, e.g., a department store or a metro station. Our approach predicts absolute scale dept…
3D ReconstructionDepth EstimationDeep Depth from Defocus: how can defocus blur improve 3D estimation using dense neural networks?
Depth estimation is of critical interest for scene understanding and accurate 3D reconstruction. Most recent approaches in depth estimation with deep learning exploit geometrical structures of standard sharp images to pr…
3D ReconstructionDepth EstimationDepth PredictionScene UnderstandingSparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image
We consider the problem of dense depth prediction from a sparse set of depth measurements and a single RGB image. Since depth estimation from monocular images alone is inherently ambiguous and unreliable, to attain a hig…
Depth EstimationDepth PredictionPredictionSimultaneous Localization and Mapping+1