A Two-Streamed Network for Estimating Fine-Scaled Depth Maps from Single RGB Images
Jun Li,Reinhard Klein,Angela Yao +2 more
- pp 3392-3400
TLDR
In this article, a fast-to-train two-streamed CNN is proposed to predict depth and depth gradients, which are then fused together into an accurate and detailed depth map.Abstract:
Estimating depth from a single RGB image is an ill-posed and inherently ambiguous problem. State-of-the-art deep learning methods can now estimate accurate 2D depth maps, but when the maps are projected into 3D, they lack local detail and are often highly distorted. We propose a fast-to-train two-streamed CNN that predicts depth and depth gradients, which are then fused together into an accurate and detailed depth map. We also define a novel set loss over multiple images; by regularizing the estimation between a common set of images, the network is less prone to overfitting and achieves better accuracy than competing methods. Experiments on the NYU Depth v2 dataset shows that our depth predictions are competitive with state-of-the-art and lead to faithful 3D projections.read more
Citations
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Proceedings ArticleDOI
Deep Ordinal Regression Network for Monocular Depth Estimation
TL;DR: Deep Ordinal Regression Network (DORN) as discussed by the authors discretizes depth and recast depth network learning as an ordinal regression problem by training the network using an ordinary regression loss, which achieves much higher accuracy and faster convergence in synch.
Proceedings ArticleDOI
Densely Connected Pyramid Dehazing Network
He Zhang,Vishal M. Patel +1 more
TL;DR: Zhang et al. as discussed by the authors proposed a Densely Connected Pyramid Dehazing Network (DCPDN), which can jointly learn the transmission map, atmospheric light and dehazing all together.
Proceedings ArticleDOI
Enforcing Geometric Constraints of Virtual Normal for Depth Prediction
TL;DR: Zhang et al. as mentioned in this paper designed a loss term that enforces one simple type of geometric constraints, namely, virtual normal directions determined by randomly sampled three points in the reconstructed 3D space.
Posted Content
From big to small: Multi-scale local planar guidance for monocular depth estimation
TL;DR: This paper proposes a network architecture that utilizes novel local planar guidance layers located at multiple stages in the decoding phase that outperforms the state-of-the-art works with significant margin evaluating on challenging benchmarks.
Proceedings ArticleDOI
Deep Depth Completion of a Single RGB-D Image
Yinda Zhang,Thomas Funkhouser +1 more
TL;DR: In this article, a deep network is trained to predict surface normals and occlusion boundaries, which are then combined with raw depth observations provided by the RGB-D camera to solve for all pixels, including those missing in the original observation.
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