Understanding the 3D structure of a scene is of vital importance, when it comes to developing fully autonomous robots. To this end, we present a novel deep learning based framework that estimates depth, surface normals and surface curvature by only using a single RGB image. To the best of our knowledge this is the first work to estimate surface curvature from colour using a machine learning approach. Additionally, we demonstrate that by tuning the network to infer well designed features, such as surface curvature, we can achieve improved performance at estimating depth and normals.This indicates that network guidance is still a useful aspect of designing and training a neural network. We run extensive experiments where the network is trained to infer different tasks while the model capacity is kept constant resulting in different feature maps based on the tasks at hand. We outperform the previous state-of-the-art benchmarks which jointly estimate depths and surface normals while predicting surface curvature in parallel.
Data driven single image surface normal estimation was first tackled by in @cite_39 . They used a SVM based detector followed by an iterative optimization scheme to extract geometrically informative primitives. proposed to use image cues of pixel-wise and segment based methods to generate a feature representation that can estimate surface normals in a boosting framework @cite_12 . A ConvNet approach to estimating surface normals in global and local scales while incorporating numerous constraints such as room layout and edge labels was taken by @cite_24 . Recently, @cite_31 showed that by combining hierarchy of features from different levels of activations in a skip-network architecture that you could generate much finer predictions for surface normals achieving state of the art results.
@cite_24 @cite_31 @cite_12 @cite_39
2952623155 2341204628 337610345
In the past few years, convolutional neural nets (CNN) have shown incredible promise for learning visual representations. In this paper, we use CNNs for the task of predicting surface normals from a single image. But what is the right architecture we should use? We propose to build upon the decades of hard work in 3D scene understanding, to design new CNN architecture for the task of surface normal estimation. We show by incorporating several constraints (man-made, manhattan world) and meaningful intermediate representations (room layout, edge labels) in the architecture leads to state of the art performance on surface normal estimation. We also show that our network is quite robust and show state of the art results on other datasets as well without any fine-tuning. We introduce an approach that leverages surface normal predictions, along with appearance cues, to retrieve 3D models for objects depicted in 2D still images from a large CAD object library. Critical to the success of our approach is the ability to recover accurate surface normals for objects in the depicted scene. We introduce a skip-network model built on the pre-trained Oxford VGG convolutional neural network (CNN) for surface normal prediction. Our model achieves state-of-the-art accuracy on the NYUv2 RGB-D dataset for surface normal prediction, and recovers fine object detail compared to previous methods. Furthermore, we develop a two-stream network over the input image and predicted surface normals that jointly learns pose and style for CAD model retrieval. When using the predicted surface normals, our two-stream network matches prior work using surface normals computed from RGB-D images on the task of pose prediction, and achieves state of the art when using RGB-D input. Finally, our two-stream network allows us to retrieve CAD models that better match the style and pose of a depicted object compared with baseline approaches. In this work we propose the method for a rather unexplored problem of computer vision - discriminatively trained dense surface normal estimation from a single image. Our method combines contextual and segment-based cues and builds a regressor in a boosting framework by transforming the problem into the regression of coefficients of a local coding. We apply our method to two challenging data sets containing images of man-made environments, the indoor NYU2 data set and the outdoor KITTI data set. Our surface normal predictor achieves results better than initially expected, significantly outperforming state-of-the-art.