This paper studies single-image depth perception in the wild, i.e., recovering depth from a single image taken in unconstrained settings. We introduce a new dataset "Depth in the Wild" consisting of images in the wild annotated with relative depth between pairs of random points. We also propose a new algorithm that learns to estimate metric depth using annotations of relative depth. Compared to the state of the art, our algorithm is simpler and performs better. Experiments show that our algorithm, combined with existing RGB-D data and our new relative depth annotations, significantly improves single-image depth perception in the wild.
Depth from a Single Image: Image-to-depth is a long-standing problem with a large body of literature @cite_0 @cite_29 @cite_15 @cite_36 @cite_9 @cite_7 @cite_40 @cite_21 @cite_4 @cite_0 @cite_11 @cite_18 @cite_26 @cite_23 @cite_33 @cite_17 . The recent convergence of deep neural networks and RGB-D datasets @cite_5 @cite_39 has led to major advances @cite_38 @cite_9 @cite_27 @cite_40 @cite_4 @cite_10 . But the networks in these previous works, with the exception of @cite_10 , were trained exclusively using ground-truth metric depth, whereas our approach uses relative depth.
@cite_36 @cite_29 @cite_5 @cite_15 @cite_10 @cite_38 @cite_18 @cite_4 @cite_21 @cite_39 @cite_23 @cite_17 @cite_26 @cite_7 @cite_40 @cite_27 @cite_33 @cite_9 @cite_0 @cite_11
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Abstract: In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision. Inferring the underlying depth map from a single image is an ill-posed and inherently ambiguous problem. State-of-the-art deep learning methods can now estimate accurate depth maps, but when projected into 3D, still lack local detail and are often highly distorted. We propose a multi-scale convolution neural network to learn from single RGB images fine-scaled depth maps that result in realistic 3D reconstructions. To encourage spatial coherency, we introduce spatial coordinate feature maps and a local relative depth constraint. In our network, the three scales are closely integrated with skip fusion layers, making it highly efficient to train with large-scale data. Experiments on the NYU Depth v2 dataset shows that our depth predictions are not only competitive with state-of-the-art but also leading to 3D reconstructions that are accurate and rich with detail. In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision. RGB-D images captured by consumer camera can provide pair-wise color and depth information but depth image usually contains strong noise and large holes. Due to different modalities of RGB-D, the intensity-guided depth enhancement easily causes artifacts and blurred edges. To solve this problem, we propose the multi-scale mutual feature convolutional neural network (MSMF-CNN) to learn essential mutual features of RGB-D. This end-to-end framework has two stages, i.e., mutual feature learning and depth regeneration. Firstly, we design two parallel separated subnetworks as intensity and depth mutual feature generator. Specifically, we design a multi-scale mutual feature generator to enforce the depth mutual feature learning and incorporate various structure characteristic. Secondly, depth transformation sub-network combines two mutual features to recover clean depth image. We tested our model on two well-known datasets in terms of PSNR, visual effect and computing speed. Compared with state-of-art algorithms, our method provides better performance of all three criteria.