Superpixels: An Evaluation of the State-of-the-Art

Superpixels group perceptually similar pixels to create visually meaningful entities while heavily reducing the number of primitives for subsequent processing steps. As of these properties, superpixel algorithms have received much attention since their naming in 2003. By today, publicly available superpixel algorithms have turned into standard tools in low-level vision. As such, and due to their quick adoption in a wide range of applications, appropriate benchmarks are crucial for algorithm selection and comparison. Until now, the rapidly growing number of algorithms as well as varying experimental setups hindered the development of a unifying benchmark. We present a comprehensive evaluation of 28 state-of-the-art superpixel algorithms utilizing a benchmark focussing on fair comparison and designed to provide new insights relevant for applications. To this end, we explicitly discuss parameter optimization and the importance of strictly enforcing connectivity. Furthermore, by extending well-known metrics, we are able to summarize algorithm performance independent of the number of generated superpixels, thereby overcoming a major limitation of available benchmarks. Furthermore, we discuss runtime, robustness against noise, blur and affine transformations, implementation details as well as aspects of visual quality. Finally, we present an overall ranking of superpixel algorithms which redefines the state-of-the-art and enables researchers to easily select appropriate algorithms and the corresponding implementations which themselves are made publicly available as part of our benchmark at davidstutz.de/projects/superpixel-benchmark/

Segmentation and semantic labelling of RGBD data with convolutional neural networks and surface fitting

We present an approach for segmentation and semantic labelling of RGBD data exploiting together geometrical cues and deep learning techniques. An initial over-segmentation is performed using spectral clustering and a set of non-uniform rational B-spline surfaces is fitted on the extracted segments. Then a convolutional neural network (CNN) receives in input colour and geometry data together with surface fitting parameters. The network is made of nine convolutional stages followed by a softmax classifier and produces a vector of descriptors for each sample. In the next step, an iterative merging algorithm recombines the output of the over-segmentation into larger regions matching the various elements of the scene. The couples of adjacent segments with higher similarity according to the CNN features are candidate to be merged and the surface fitting accuracy is used to detect which couples of segments belong to the same surface. Finally, a set of labelled segments is obtained by combining the segmentation output with the descriptors from the CNN. Experimental results show how the proposed approach outperforms state-of-the-art methods and provides an accurate segmentation and labelling

Joint segmentation of color and depth data based on splitting and merging driven by surface fitting

This paper proposes a segmentation scheme based on the joint usage of color and depth data together with a 3D surface estimation scheme. Firstly a set of multi-dimensional vectors is built from color, geometry and surface orientation information. Normalized cuts spectral clustering is then applied in order to recursively segment the scene in two parts thus obtaining an over-segmentation. This procedure is followed by a recursive merging stage where close segments belonging to the same object are joined together. At each step of both procedures a NURBS model is fitted on the computed segments and the accuracy of the fitting is used as a measure of the plausibility that a segment represents a single surface or object. By comparing the accuracy to the one at the previous step, it is possible to determine if each splitting or merging operation leads to a better scene representation and consequently whether to perform it or not. Experimental results show how the proposed method provides an accurate and reliable segmentation

Joint segmentation of color and depth data based on splitting and merging driven by surface fitting

This paper proposes a segmentation scheme based on the joint usage of color and depth data together with a 3D surface estimation scheme. Firstly a set of multi-dimensional vectors is built from color, geometry and surface orientation information. Normalized cuts spectral clustering is then applied in order to recursively segment the scene in two parts thus obtaining an over-segmentation. This procedure is followed by a recursive merging stage where close segments belonging to the same object are joined together. At each step of both procedures a NURBS model is fitted on the computed segments and the accuracy of the fitting is used as a measure of the plausibility that a segment represents a single surface or object. By comparing the accuracy to the one at the previous step, it is possible to determine if each splitting or merging operation leads to a better scene representation and consequently whether to perform it or not. Experimental results show how the proposed method provides an accurate and reliable segmentation

Fine-Scaled 3D Geometry Recovery from Single RGB Images

3D geometry recovery from single RGB images is a highly ill-posed and inherently ambiguous problem, which has been a challenging research topic in computer vision for several decades. When fine-scaled 3D geometry is required, the problem become even more difficult. 3D geometry recovery from single images has the objective of recovering geometric information from a single photograph of an object or a scene with multiple objects. The geometric information that is to be retrieved can be of different representations such as surface meshes, voxels, depth maps or 3D primitives, etc. In this thesis, we investigate fine-scaled 3D geometry recovery from single RGB images for three categories: facial wrinkles, indoor scenes and man-made objects. Since each category has its own particular features, styles and also variations in representation, we propose different strategies to handle different 3D geometry estimates respectively. We present a lightweight non-parametric method to generate wrinkles from monocular Kinect RGB images. The key lightweight feature of the method is that it can generate plausible wrinkles using exemplars from one high quality 3D face model with textures. The local geometric patches from the source could be copied to synthesize different wrinkles on the blendshapes of specific users in an offline stage. During online tracking, facial animations with high quality wrinkle details can be recovered in real-time as a linear combination of these personalized wrinkled blendshapes. We propose a fast-to-train two-streamed CNN with multi-scales, which predicts both dense depth map and depth gradient for single indoor scene images.The depth and depth gradient are then fused together into a more accurate and detailed depth map. We introduce a novel set loss over multiple related 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. Fine-scaled 3D point cloud could be produced by re-projection to 3D using the known camera parameters. To handle highly structured man-made objects, we introduce a novel neural network architecture for 3D shape recovering from a single image. We develop a convolutional encoder to map a given image to a compact code. Then an associated recursive decoder maps this code back to a full hierarchy, resulting a set of bounding boxes to represent the estimated shape. Finally, we train a second network to predict the fine-scaled geometry in each bounding box at voxel level. The per-box volumes are then embedded into a global one, and from which we reconstruct the final meshed model. Experiments on a variety of datasets show that our approaches can estimate fine-scaled geometry from single RGB images for each category successfully, and surpass state-of-the-art performance in recovering faithful 3D local details with high resolution mesh surface or point cloud