Mesh labeling is the key problem of classifying the facets of a 3D mesh with a label among a set of possible ones. State-of-the-art methods model mesh labeling as a Markov Random Field over the facets. These algorithms map image segmentations to the mesh by minimizing an energy function that comprises a data term, a smoothness terms, and class-specific priors. The latter favor a labeling with respect to another depending on the orientation of the facet normals. In this paper we propose a novel energy term that acts as a prior, but does not require any prior knowledge about the scene nor scene-specific relationship among classes. It bootstraps from a coarse mapping of the 2D segmentations on the mesh, and it favors the facets to be labeled according to the statistics of the mesh normals in their neighborhood. We tested our approach against five different datasets and, even if we do not inject prior knowledge, our method adapts to the data and overcomes the state-of-the-art.
Herman al @cite_35 and McCormac al @cite_12 fuse incrementally the 2D images segmentation into the reconstruction. These methods, as well as @cite_25 , benefit from the vast literature about 2D segmentation algorithms for 2D image segmentation @cite_36 @cite_7 @cite_27 . However, the output of @cite_35 and @cite_12 are not continuous triangular meshes, but, respectively, a point cloud and a surfel model. Tung al @cite_34 proposed an interesting method that estimates the unary potential of a CRF without any classifier, but by comparing the regions of the image computed as in @cite_26 , with a database of annotated examples. However they do not use any semantic prior to add robustness of the final outcome.
@cite_35 @cite_26 @cite_7 @cite_36 @cite_27 @cite_34 @cite_25 @cite_12
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Dense semantic segmentation of 3D point clouds is a challenging task. Many approaches deal with 2D semantic segmentation and can obtain impressive results. With the availability of cheap RGB-D sensors the field of indoor semantic segmentation has seen a lot of progress. Still it remains unclear how to deal with 3D semantic segmentation in the best way. We propose a novel 2D-3D label transfer based on Bayesian updates and dense pairwise 3D Conditional Random Fields. This approach allows us to use 2D semantic segmentations to create a consistent 3D semantic reconstruction of indoor scenes. To this end, we also propose a fast 2D semantic segmentation approach based on Randomized Decision Forests. Furthermore, we show that it is not needed to obtain a semantic segmentation for every frame in a sequence in order to create accurate semantic 3D reconstructions. We evaluate our approach on both NYU Depth datasets and show that we can obtain a significant speed-up compared to other methods. Scene parsing is the problem of assigning a semantic label to every pixel in an image. Though an ambitious task, impressive advances have been made in recent years, in particular in scalable nonparametric techniques suitable for open-universe databases. This paper presents the CollageParsing algorithm for scalable nonparametric scene parsing. In contrast to common practice in recent nonparametric approaches, CollageParsing reasons about mid-level windows that are designed to capture entire objects, instead of low-level superpixels that tend to fragment objects. On a standard benchmark consisting of outdoor scenes from the LabelMe database, CollageParsing achieves state-of-the-art nonparametric scene parsing results with 7 to 11 higher average per-class accuracy than recent nonparametric approaches. We propose a structured prediction architecture, which exploits the local generic features extracted by Convolutional Neural Networks and the capacity of Recurrent Neural Networks (RNN) to retrieve distant dependencies. The proposed architecture, called ReSeg, is based on the recently introduced ReNet model for image classification. We modify and extend it to perform the more challenging task of semantic segmentation. Each ReNet layer is composed of four RNN that sweep the image horizontally and vertically in both directions, encoding patches or activations, and providing relevant global information. Moreover, ReNet layers are stacked on top of pre-trained convolutional layers, benefiting from generic local features. Upsampling layers follow ReNet layers to recover the original image resolution in the final predictions. The proposed ReSeg architecture is efficient, flexible and suitable for a variety of semantic segmentation tasks. We evaluate ReSeg on several widely-used semantic segmentation datasets: Weizmann Horse, Oxford Flower, and CamVid; achieving state-of-the-art performance. Results show that ReSeg can act as a suitable architecture for semantic segmentation tasks, and may have further applications in other structured prediction problems. The source code and model hyperparameters are available on this https URL In this work we address the task of semantic image segmentation with Deep Learning and make three main contributions that are experimentally shown to have substantial practical merit. First , we highlight convolution with upsampled filters, or ‘atrous convolution’, as a powerful tool in dense prediction tasks. Atrous convolution allows us to explicitly control the resolution at which feature responses are computed within Deep Convolutional Neural Networks. It also allows us to effectively enlarge the field of view of filters to incorporate larger context without increasing the number of parameters or the amount of computation. Second , we propose atrous spatial pyramid pooling (ASPP) to robustly segment objects at multiple scales. ASPP probes an incoming convolutional feature layer with filters at multiple sampling rates and effective fields-of-views, thus capturing objects as well as image context at multiple scales. Third , we improve the localization of object boundaries by combining methods from DCNNs and probabilistic graphical models. The commonly deployed combination of max-pooling and downsampling in DCNNs achieves invariance but has a toll on localization accuracy. We overcome this by combining the responses at the final DCNN layer with a fully connected Conditional Random Field (CRF), which is shown both qualitatively and quantitatively to improve localization performance. Our proposed “DeepLab” system sets the new state-of-art at the PASCAL VOC-2012 semantic image segmentation task, reaching 79.7 percent mIOU in the test set, and advances the results on three other datasets: PASCAL-Context, PASCAL-Person-Part, and Cityscapes. All of our code is made publicly available online. Image semantic segmentation is more and more being of interest for computer vision and machine learning researchers. Many applications on the rise need accurate and efficient segmentation mechanisms: autonomous driving, indoor navigation, and even virtual or augmented reality systems to name a few. This demand coincides with the rise of deep learning approaches in almost every field or application target related to computer vision, including semantic segmentation or scene understanding. This paper provides a review on deep learning methods for semantic segmentation applied to various application areas. Firstly, we describe the terminology of this field as well as mandatory background concepts. Next, the main datasets and challenges are exposed to help researchers decide which are the ones that best suit their needs and their targets. Then, existing methods are reviewed, highlighting their contributions and their significance in the field. Finally, quantitative results are given for the described methods and the datasets in which they were evaluated, following up with a discussion of the results. At last, we point out a set of promising future works and draw our own conclusions about the state of the art of semantic segmentation using deep learning techniques. We present a novel model-free method for online 3D semantic scene parsing from video sequences. MF3D (Model-Free 3D) is different from conventional methods for 3D scene parsing in that voxel labelling is approached via search-based label transfer instead of discriminative classification. This non-parametric approach makes MF3D easy to scale with an online growth in the database, as no model re-training is required with the addition of new examples or categories. Experimental results on the KITTI benchmark demonstrate that our model-free approach enables accurate online 3D scene parsing while retaining extensibility to new categories. In addition, we show that unsupervised binary encoding (hashing) techniques can be easily incorporated into our framework for scalability to larger databases. In this paper we propose a robust algorithm that generates an efficient and accurate dense 3D reconstruction with associated semantic labellings. Intelligent autonomous systems require accurate 3D reconstructions for applications such as navigation and localisation. Such systems also need to recognise their surroundings in order to identify and interact with objects of interest. Considerable emphasis has been given to generating a good reconstruction but less effort has gone into generating a 3D semantic model. The inputs to our algorithm are street level stereo image pairs acquired from a camera mounted on a moving vehicle. The depth-maps, generated from the stereo pairs across time, are fused into a global 3D volume online in order to accommodate arbitrary long image sequences. The street level images are automatically labelled using a Conditional Random Field (CRF) framework exploiting stereo images, and label estimates are aggregated to annotate the 3D volume. We evaluate our approach on the KITTI odometry dataset and have manually generated ground truth for object class segmentation. Our qualitative evaluation is performed on various sequences of the dataset and we also quantify our results on a representative subset. Ever more robust, accurate and detailed mapping using visual sensing has proven to be an enabling factor for mobile robots across a wide variety of applications. For the next level of robot intelligence and intuitive user interaction, maps need to extend beyond geometry and appearance — they need to contain semantics. We address this challenge by combining Convolutional Neural Networks (CNNs) and a state-of-the-art dense Simultaneous Localization and Mapping (SLAM) system, ElasticFusion, which provides long-term dense correspondences between frames of indoor RGB-D video even during loopy scanning trajectories. These correspondences allow the CNN's semantic predictions from multiple view points to be probabilistically fused into a map. This not only produces a useful semantic 3D map, but we also show on the NYUv2 dataset that fusing multiple predictions leads to an improvement even in the 2D semantic labelling over baseline single frame predictions. We also show that for a smaller reconstruction dataset with larger variation in prediction viewpoint, the improvement over single frame segmentation increases. Our system is efficient enough to allow real-time interactive use at frame-rates of ≈25Hz.