mtybilly/Instruct2DS huggingface dataset:
Spatial Semantic Regularisation for Large Scale Object Detection
Authors: Damian Mrowca, Marcus Rohrbach, Judy Hoffman, Ronghang Hu, Kate Saenko, Trevor Darrell
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: Large scale object detection with thousands of classes introduces the problem of many contradicting false positive detections, which have to be suppressed. Class-independent non-maximum suppression has traditionally been used for this step, but it does not scale well as the number of classes grows. Traditional non-maximum suppression does not consider label- and instance-level relationships nor does it allow an exploitation of the spatial layout of detection proposals. We propose a new multi-class spatial semantic regularisation method based on affinity propagation clustering, which simultaneously optimises across all categories and all proposed locations in the image, to improve both the localisation and categorisation of selected detection proposals.  Constraints are shared across the labels through the semantic WordNet hierarchy. Our approach proves to be especially useful in large scale settings with thousands of classes, where spatial and semantic interactions are very frequent and only weakly supervised detectors can be built due to a lack of bounding box annotations. Detection experiments are conducted on the ImageNet and COCO dataset, and in settings with thousands of detected categories. Our method provides a significant precision improvement by reducing false positives, while simultaneously improving the recall.
Human Pose Estimation in Videos
Authors: Dong Zhang, Mubarak Shah
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: In this paper, we present a method to estimate a sequence of human poses in unconstrained videos. In contrast to the commonly employed graph optimization framework, which is NP-hard and needs approximate solutions, we formulate this problem into a unified two stage tree-based optimization problem for which an efficient and exact solution exists. Although the proposed method finds an exact solution, it does not sacrifice the ability to model the spatial and temporal constraints between body parts in the video frames; indeed it even models the symmetric parts better than the existing methods. The proposed method is based on two main ideas: `Abstraction' and `Association' to enforce the intra- and inter-frame body part constraints respectively without inducing extra computational complexity to the polynomial time solution. Using the idea of `Abstraction', a new concept of `abstract body part' is introduced to model not only the tree based body part structure similar to existing methods, but also extra constraints between symmetric parts. Using the idea of `Association', the optimal tracklets are generated for each abstract body part, in order to enforce the spatiotemporal constraints between body parts in adjacent frames. Finally, a sequence of the best poses is inferred from the abstract body part tracklets through the tree-based optimization. We evaluated the proposed method on three publicly available video based human pose estimation datasets, and obtained dramatically improved performance compared to the state-of-the-art methods.
Contour Box: Rejecting Object Proposals Without Explicit Closed Contours
Authors: Cewu Lu, Shu Liu, Jiaya Jia, Chi-Keung Tang
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: Closed contour is an important objectness indicator. We propose a new measure subject to the completeness and tightness constraints, where the optimized closed contour should be tightly bounded within an object proposal. The closed contour measure is defined using closed path integral, and we solve the optimization problem efficiently in polar coordinate system with a global optimum guaranteed. Extensive experiments show that our method can reject a large number of false proposals, and achieve over 6% improvement in object recall at the challenging overlap threshold 0.8 on the VOC 2007 test dataset.
Registering Images to Untextured Geometry Using Average Shading Gradients
Authors: Tobias Plotz, Stefan Roth
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: Many existing approaches for image-to-geometry registration assume that either a textured 3D model or a good initial guess of the 3D pose is available to bootstrap the registration process. In this paper we consider the registration of photographs to 3D models even when no texture information is available. This is very challenging as we cannot rely on texture gradients, and even shading gradients are hard to estimate since the lighting conditions are unknown. To that end, we propose average shading gradients, a rendering technique that estimates the average gradient magnitude over all lighting directions under Lambertian shading. We use this gradient representation as the building block of a registration pipeline based on matching sparse features. To cope with inevitable false matches due to the missing texture information and to increase robustness, the pose of the 3D model is estimated in two stages. Coarse pose hypotheses are first obtained from a single correct match each, subsequently refined using SIFT flow, and finally verified. We apply our algorithm to registering images of real-world objects to untextured 3D meshes of limited accuracy.
Robust Nonrigid Registration by Convex Optimization
Authors: Qifeng Chen, Vladlen Koltun
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: We present an approach to nonrigid registration of 3D surfaces. We cast isometric embedding as MRF optimization and apply efficient global optimization algorithms based on linear programming relaxations. The Markov random field perspective suggests a natural connection with robust statistics and motivates robust forms of the intrinsic distortion functional. Our approach outperforms a large body of prior work by a significant margin, increasing registration precision on real data by a factor of 3.
Robust and Optimal Sum-of-Squares-Based Point-to-Plane Registration of Image Sets and Structured Scenes
Authors: Danda Pani Paudel, Adlane Habed, Cedric Demonceaux, Pascal Vasseur
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: This paper deals with the problem of registering a known structured 3D scene and its metric Structure-from-Motion (SfM) counterpart. The proposed work relies on a prior plane segmentation of the 3D scene and aligns the data obtained from both modalities by solving the point-to-plane assignment problem. An inliers-maximization approach within a Branch-and-Bound (BnB) search scheme is adopted. For the first time in this paper, a Sum-of-Squares optimization theory framework is employed for identifying point-to-plane mismatches (i.e. outliers) with certainty. This allows us to iteratively build potential inliers sets and converge to the solution satisfied by the largest number of point-to-plane assignments. Furthermore, our approach is boosted by new plane visibility conditions which are also introduced in this paper. Using this framework, we solve the registration problem in two cases: (i) a set of putative point-to-plane correspondences (with possibly overwhelmingly many outliers) is given as input and (ii) no initial correspondences are given. In both cases, our approach yields outstanding results in terms of robustness and optimality.
MeshStereo: A Global Stereo Model With Mesh Alignment Regularization for View Interpolation
Authors: Chi Zhang, Zhiwei Li, Yanhua Cheng, Rui Cai, Hongyang Chao, Yong Rui
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: We present a novel global stereo model designed for view interpolation. Unlike existing stereo models which only output a disparity map, our model is able to output a 3D triangular mesh, which can be directly used for view interpolation. To this aim, we partition the input stereo images into 2D triangles with shared vertices. Lifting the 2D triangulation to 3D naturally generates a corresponding mesh. A technical difficulty is to properly split vertices to multiple copies when they appear at depth discontinuous boundaries. To deal with this problem, we formulate our objective as a two-layer MRF, with the upper layer modeling the splitting properties of the vertices and the lower layer optimizing a region-based stereo matching. Experiments on the Middlebury and the Herodion datasets demonstrate that our model is able to synthesize visually coherent new view angles with high PSNR, as well as outputting high quality disparity maps which rank at the first place on the new challenging high resolution Middlebury 3.0 benchmark.
CV-HAZOP: Introducing Test Data Validation for Computer Vision
Authors: Oliver Zendel, Markus Murschitz, Martin Humenberger, Wolfgang Herzner
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: Test data plays an important role in computer vision (CV) but is plagued by two questions: Which situations should be covered by the test data and have we tested enough to reach a conclusion? In this paper we propose a new solution answering these questions using a standard procedure devised by the safety community to validate complex systems: The Hazard and Operability Analysis (HAZOP). It is designed to systematically search and identify difficult, performance-decreasing situations and aspects. We introduce a generic CV model that creates the basis for the hazard analysis and, for the first time, apply an extensive HAZOP to the CV domain. The result is a publicly available checklist with more than 900 identified individual hazards. This checklist can be used to evaluate existing test datasets by quantifying the amount of covered hazards. We evaluate our approach by first analyzing and annotating the popular stereo vision test datasets Middlebury and KITTI. Second, we compare the performance of six popular stereo matching algorithms at the identified hazards from our checklist with their average performance and show, as expected, a clear negative influence of the hazards. The presented approach is a useful tool to evaluate and improve test datasets and creates a common basis for future dataset designs.
Structure From Motion Using Structure-Less Resection
Authors: Enliang Zheng, Changchang Wu
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: This paper proposes a new incremental structure from motion (SfM) algorithm based on a novel structure-less camera resection technique. Traditional methods rely on 2D-3D correspondences to compute the pose of candidate cameras using PnP.  In this work, we take the collection of already reconstructed cameras as a generalized camera, and  determine the absolute pose of a candidate pinhole camera from pure 2D correspondences, which we call it  semi-generalized camera pose problem. We present the minimal solvers of the new problem for both calibrated and partially calibrated (unknown focal length) pinhole cameras. By integrating these new algorithms in an incremental SfM system, we go beyond the state-of-art methods with the capability of reconstructing cameras without 2D-3D correspondences.  Large-scale real image experiments show that our new SfM system significantly improves the completeness of 3D reconstruction over the standard approach.
Joint Camera Clustering and Surface Segmentation for Large-Scale Multi-View Stereo
Authors: Runze Zhang, Shiwei Li, Tian Fang, Siyu Zhu, Long Quan
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: In this paper, we propose an optimal decomposition approach to large-scale multi-view stereo from an initial sparse reconstruction. The success of the approach depends on the introduction of surface-segmentation-based camera clustering rather than sparse-point-based camera clustering, which suffers from the problems of non-uniform reconstruction coverage ratio and high redundancy. In details, we introduce three criteria for camera clustering and surface segmentation for reconstruction, and then we formulate these criteria into an energy minimization problem under constraints. To solve this problem, we propose a joint optimization in a hierarchical framework to obtain the final surface segments and corresponding optimal camera clusters. On each level of the hierarchical framework, the camera clustering problem is formulated as a parameter estimation problem of a probability model solved by a General Expectation-Maximization algorithm and the surface segmentation problem is formulated as a Markov Random Field model based on the probability estimated by the previous camera clustering process. The experiments on several Internet datasets and aerial photo datasets demonstrate that the proposed approach method generates more uniform and complete dense reconstruction with less redundancy, resulting in more efficient multi-view stereo algorithm.
Higher-Order CRF Structural Segmentation of 3D Reconstructed Surfaces
Authors: Jingbo Liu, Jinglu Wang, Tian Fang, Chiew-Lan Tai, Long Quan
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: In this paper, we propose a structural segmentation algorithm to partition multi-view stereo reconstructed surfaces of large-scale urban environments into structural segments. Each segment corresponds to a structural component describable by a surface primitive of up to the second order. This segmentation is for use in subsequent urban object modeling, vectorization, and recognition.  To overcome the high geometrical and topological noise levels in the 3D reconstructed urban surfaces, we formulate the structural segmentation as a higher-order Conditional Random Field (CRF) labeling problem. It not only incorporates classical lower-order 2D and 3D local cues, but also encodes contextual geometric regularities to disambiguate the noisy local cues. A general higher-order CRF is difficult to solve. We develop a bottom-up progressive approach through a patch-based surface representation, which iteratively evolves from the initial mesh triangles to the final segmentation. Each iteration alternates between performing a prior discovery step, which finds the contextual regularities of the patch-based representation, and an inference step that leverages the regularities as higher-order priors to construct a more stable and regular segmentation.  The efficiency and robustness of the proposed method is extensively demonstrated on real reconstruction models, yielding significantly better performance than classical mesh segmentation methods.
Hyperpoints and Fine Vocabularies for Large-Scale Location Recognition
Authors: Torsten Sattler, Michal Havlena, Filip Radenovic, Konrad Schindler, Marc Pollefeys
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: Structure-based localization is the task of finding the absolute pose of a given query image w.r.t. a pre-computed 3D model. While this is almost trivial at small scale, special care must be taken as the size of the 3D model grows, because straight-forward descriptor matching becomes ineffective due to the large memory footprint of the model, as well as the strictness of the ratio test in 3D. Recently, several authors have tried to overcome these problems, either by a smart compression of the 3D model or by clever sampling strategies for geometric verification. Here we explore an orthogonal strategy, which uses all the 3D points and standard sampling, but performs feature matching implicitly, by quantization into a fine vocabulary. We show that although this matching is ambiguous and gives rise to 3D hyperpoints when matching each 2D query feature in isolation, a simple voting strategy, which enforces the fact that the selected 3D points shall be co-visible, can reliably find a locally unique 2D-3D point assignment. Experiments on two large-scale datasets demonstrate that our method achieves state-of-the-art performance, while the memory footprint is greatly reduced, since only visual word labels but no 3D point descriptors need to be stored.
Globally Optimal 2D-3D Registration From Points or Lines Without Correspondences
Authors: Mark Brown, David Windridge, Jean-Yves Guillemaut
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: We present a novel approach to 2D-3D registration from points or lines without correspondences. While there exist established solutions in the case where correspondences are known, there are many situations where it is not possible to reliably extract such correspondences across modalities, thus requiring the use of a correspondence-free registration algorithm. Existing correspondence-free methods rely on local search strategies and consequently have no guarantee of finding the optimal solution. In contrast, we present the first globally optimal approach to 2D-3D registration without correspondences, achieved by a Branch-and-Bound algorithm. Furthermore, a deterministic annealing procedure is proposed to speed up the nested branch-and-bound algorithm used. The theoretical and practical advantages this brings are demonstrated on a range of synthetic and real data where it is observed that the proposed approach is significantly more robust to high proportions of outliers compared to existing approaches.
The HCI Stereo Metrics: Geometry-Aware Performance Analysis of Stereo Algorithms
Authors: Katrin Honauer, Lena Maier-Hein, Daniel Kondermann
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: Performance characterization of stereo methods is mandatory to decide which algorithm is useful for which application. Prevalent benchmarks mainly use the root mean squared error (RMS) with respect to ground truth disparity maps to quantify algorithm performance.  We show that the RMS is of limited expressiveness for algorithm selection and introduce the HCI Stereo Metrics.  These metrics assess stereo results by harnessing three semantic cues: depth discontinuities, planar surfaces, and fine geometric structures. For each cue, we extract the relevant set of pixels from existing ground truth. We then apply our evaluation functions to quantify characteristics such as edge fattening and surface smoothness.  We demonstrate that our approach supports practitioners in selecting the most suitable algorithm for their application. Using the new Middlebury dataset, we show that rankings based on our metrics reveal specific algorithm strengths and weaknesses which are not quantified by existing metrics. We finally show how stacked bar charts and radar charts visually support multidimensional performance evaluation.  An interactive stereo benchmark based on the proposed metrics and visualizations is available at:  http://hci.iwr.uni-heidelberg.de/stereometrics
Merging the Unmatchable: Stitching Visually Disconnected SfM Models
Authors: Andrea Cohen, Torsten Sattler, Marc Pollefeys
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: Recent advances in Structure-from-Motion not only enable the reconstruction of large scale scenes, but are also able to detect ambiguous structures caused by repeating elements that might result in incorrect reconstructions. Yet, it is not always possible to fully reconstruct a scene. The images required to merge different sub-models might be missing or it might be impossible to acquire such images in the first place due to occlusions or the structure of the scene. The problem of aligning multiple reconstructions that do not have visual overlap is impossible to solve in general. An important variant of this problem is the case in which individual sides of a building can be reconstructed but not joined due to the missing visual overlap. In this paper, we present a combinatorial approach for solving this variant by automatically stitching multiple sides of a building together. Our approach exploits symmetries and semantic information to reason about the possible geometric relations between the individual  models. We show that our approach is able to reconstruct complete building models where traditional SfM ends up with disconnected building sides.
3D Fragment Reassembly Using Integrated Template Guidance and Fracture-Region Matching
Authors: Kang Zhang, Wuyi Yu, Mary Manhein, Warren Waggenspack, Xin Li
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: This paper studies matching of fragmented objects to recompose their original geometry. Solving this geometric reassembly problem has direct applications in archaeology and forensic investigation in the computer-aided restoration of damaged artifacts and evidence. We develop a new algorithm to effectively integrate both guidance from a template and from matching of adjacent pieces' fracture-regions. First, we compute partial matchings between fragments and a template, and pairwise matchings among fragments. Many potential matches are obtained and then selected/refined in a multi-piece matching stage to maximize global groupwise matching consistency. This pipeline is effective in composing fragmented thin-shell objects containing small pieces, whose pairwise matching is usually unreliable and ambiguous and hence their reassembly remains challenging to the existing algorithms.
Procedural Editing of 3D Building Point Clouds
Authors: Ilke Demir, Daniel G. Aliaga, Bedrich Benes
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: Thanks to the recent advances in computational photography and remote sensing, point clouds of buildings are becoming increasingly available, yet their processing poses various challenges. In our work, we tackle the problem of point cloud completion and editing and we approach it via inverse procedural modeling. Contrary to the previous work, our approach operates directly on the point cloud without an intermediate triangulation. Our approach consists of 1) semi-automatic segmentation of the input point cloud with segment comparison and template matching to detect repeating structures, 2) a consensus-based voting schema and a pattern extraction algorithm to discover completed terminal geometry and their patterns of usage, all encoded into a context-free grammar, and 3) an interactive editing tool where the user can create new point clouds by using procedural copy and paste operations, and smart resizing. We demonstrate our approach on editing of building models with up to 1.8M points. In our implementation, preprocessing takes up to several minutes and a single editing operation needs from one second to one minute depending on the model size and the operation type.
Semantically-Aware Aerial Reconstruction From Multi-Modal Data
Authors: Randi Cabezas, Julian Straub, John W. Fisher III
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: We consider a methodology for integrating multiple sensors along with semantic information to enhance scene representations.  We propose a probabilistic generative model for inferring semantically-informed aerial reconstructions from multi-modal data within a consistent mathematical framework. The approach, called Semantically- Aware Aerial Reconstruction (SAAR), not only exploits inferred scene geometry, appearance, and semantic observations to obtain a meaningful categorization of the data, but also extends previously proposed methods by imposing structure on the prior over geometry, appearance, and semantic labels.  This leads to more accurate reconstructions and the ability to fill in missing contextual labels via joint sensor and semantic information. We introduce a new multi-modal synthetic dataset in order to provide quantitative performance analysis.  Additionally, we apply the model to real-world data and exploit OpenStreetMap as a source of semantic observations. We show quantitative improvements in reconstruction accuracy of large-scale urban scenes from the combination of LiDAR, aerial photography, and semantic data.  Furthermore, we demonstrate the model's ability to fill in for missing sensed data, leading to more interpretable reconstructions.
Guaranteed Outlier Removal for Rotation Search
Authors: Alvaro Parra Bustos, Tat-Jun Chin
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: Rotation search has become a core routine for solving many computer vision problems. The aim is to rotationally align two input point sets with correspondences. Recently, there is significant interest in developing globally optimal rotation search algorithms. A notable weakness of global algorithms, however, is their relatively high computational cost, especially on large problem sizes and data with a high proportion of outliers. In this paper, we propose a novel outlier removal technique for rotation search. Our method guarantees that any correspondence it discards as an outlier does not exist in the inlier set of the globally optimal rotation for the original data. Based on simple geometric operations, our algorithm is deterministic and fast. Used as a preprocessor to prune a large portion of the outliers from the input data, our method enables substantial speed-up of rotation search algorithms without compromising global optimality. We demonstrate the efficacy of our method in various synthetic and real data experiments.
Peeking Template Matching for Depth Extension
Authors: Simon Korman, Eyal Ofek, Shai Avidan
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: We propose a method that extends a given depth image into regions in 3D that are not visible from the point of view of the camera. The algorithm detects repeated 3D structures in the visible scene and suggests a set of 3D extension hypotheses, which are then combined together through a global 3D MRF discrete optimization. The recovered global 3D surface is consistent with both the input depth map and the hypotheses.  A key component of this work is a novel 3D template matcher that is used to detect repeated 3D structure in the scene and to suggest the hypotheses. A unique property of this matcher is that it can handle depth uncertainty. This is crucial because the matcher is required to ``peek around the corner'', as it operates at the boundaries of the visible 3D scene where depth information is missing. The proposed matcher is fast and is guaranteed to find an approximation to the globally optimal solution.  We demonstrate on real-world data that our algorithm is capable of completing a full 3D scene from a single depth image and can synthesize a full depth map from a novel viewpoint of the scene. In addition, we report results on an extensive synthetic set of 3D shapes, which allows us to evaluate the method both qualitatively and quantitatively.
Deformable 3D Fusion: From Partial Dynamic 3D Observations to Complete 4D Models
Authors: Weipeng Xu, Mathieu Salzmann, Yongtian Wang, Yue Liu
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: Capturing the 3D motion of dynamic, non-rigid objects has attracted significant attention in computer vision. Existing methods typically require either complete 3D volumetric observations, or a shape template. In this paper, we introduce a template-less 4D reconstruction method that incrementally fuses highly-incomplete 3D observations of a deforming object, and generates a complete, temporally-coherent shape representation of the object. To this end, we design an online algorithm that alternatively registers new observations to the current model estimate and updates the model. We demonstrate the effectiveness of our approach at reconstructing non-rigidly moving objects from highly-incomplete measurements on both sequences of partial 3D point clouds and Kinect videos.
Non-Parametric Structure-Based Calibration of Radially Symmetric Cameras
Authors: Federico Camposeco, Torsten Sattler, Marc Pollefeys
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: We propose a novel two-step method for estimating the intrinsic and extrinsic calibration of any radially symmetric camera, including non-central systems. The first step consists of estimating the camera pose, given a Structure from Motion (SfM) model, up to the translation along the optical axis. As a second step, we obtain the calibration by finding the translation of the camera center using an ordering constraint. The method makes use of the 1D radial camera model, which allows us to effectively handle any radially symmetric camera, including non-central ones. Using this ordering constraint, we show that the we are able to calibrate several different (central and non-central) Wide Field of View (WFOV) cameras, including fisheye, hyper-catadioptric and spherical catadioptric cameras, as well as pinhole cameras, using a single image or jointly solving for several views.
Exploiting Object Similarity in 3D Reconstruction
Authors: Chen Zhou, Fatma Guney, Yizhou Wang, Andreas Geiger
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: Despite recent progress, reconstructing outdoor scenes in 3D from movable platforms remains a highly difficult endeavour. Challenges include low frame rates, occlusions, large distortions and difficult lighting conditions. In this paper, we leverage the fact that the larger the reconstructed area, the more likely objects of similar type and shape will occur in the scene. This is particularly true for outdoor scenes where buildings and vehicles often suffer from missing texture or reflections, but share similarity in 3D shape. We take advantage of this shape similarity by localizing objects using detectors and jointly reconstructing them while learning a volumetric model of their shape. This allows us to reduce noise while completing missing surfaces as objects of similar shape benefit from all observations for the respective category. We evaluate our approach with respect to LIDAR ground truth on a novel challenging suburban dataset and show its advantages over the state-of-the-art.
You Are Here: Mimicking the Human Thinking Process in Reading Floor-Plans
Authors: Hang Chu, Dong Ki Kim, Tsuhan Chen
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: A human can easily find his or her way in an unfamiliar building, by walking around and reading the floor-plan. We try to mimic and automate this human thinking process. More precisely, we introduce a new and useful task of locating an user in the floor-plan, by using only a camera and a floor-plan without any other prior information. We address the problem with a novel matching-localization algorithm that is inspired by human logic. We demonstrate through experiments that our method outperforms state-of-the-art floor-plan-based localization methods by a large margin, while also being highly efficient for real-time applications.
MAP Disparity Estimation Using Hidden Markov Trees
Authors: Eric T. Psota, Jedrzej Kowalczuk, Mateusz Mittek, Lance C. Perez
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: A new method is introduced for stereo matching that operates on minimum spanning trees (MSTs) generated from the images. Disparity maps are represented as a collection of hidden states on MSTs, and each MST is modeled as a hidden Markov tree. An efficient recursive message-passing scheme designed to operate on hidden Markov trees, known as the upward-downward algorithm, is used to compute the maximum a posteriori (MAP) disparity estimate at each pixel. The messages processed by the upward-downward algorithm involve two types of probabilities: the probability of a pixel having a particular disparity given a set of per-pixel matching costs, and the probability of a disparity transition between a pair of connected pixels given their similarity. The distributions of these probabilities are modeled from a collection of images with ground truth disparities. Performance evaluation using the Middlebury stereo benchmark version 3 demonstrates that the proposed method ranks second and third in terms of overall accuracy when evaluated on the training and test image sets, respectively.
Wide Baseline Stereo Matching With Convex Bounded Distortion Constraints
Authors: Meirav Galun, Tal Amir, Tal Hassner, Ronen Basri, Yaron Lipman
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: Finding correspondences in wide baseline setups is a challenging problem. Existing approaches have focused largely on developing better feature descriptors for correspondence and on accurate recovery of epipolar line constraints. This paper focuses on the challenging problem of finding correspondences once approximate epipolar constraints are given. We introduce a novel method that integrates a deformation model. Specifically, we formulate the problem as finding the largest number of corresponding points related by a bounded distortion map that obeys the given epipolar constraints. We show that, while the set of bounded distortion maps is not convex, the subset of maps that obey the epipolar line constraints is convex, allowing us to introduce an efficient algorithm for matching. We further utilize a robust cost function for matching and employ majorization-minimization for its optimization. Our experiments indicate that our method finds significantly more accurate maps than existing approaches.
Interactive Visual Hull Refinement for Specular and Transparent Object Surface Reconstruction
Authors: Xinxin Zuo, Chao Du, Sen Wang, Jiangbin Zheng, Ruigang Yang
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: In this paper we present a method of using standard multi-view images for 3D surface reconstruction of non-Lambertian objects. We extend the original visual hull concept to incorporate 3D cues presented by internal occluding contours, i.e., occluding contours that are inside the object's silhouettes. We discovered that these internal contours, which are results of convex parts on an object's surface, can lead to a tighter fit than the original visual hull. We formulated a new visual hull refinement scheme - Locally Convex Carving that can completely reconstruct concavity caused by two or more intersecting convex surfaces. In addition we develop a novel approach for contour tracking given labeled contours in sparse key frames. It is designed specifically for highly specular or transparent objects, for which assumptions made in traditional contour detection/tracking methods, such as highest gradient and stationary texture edges, are no longer valid. It is formulated as an energy minimization function where several novel terms are developed to increase robustness. Based on the two core algorithms, we have developed an interactive system for 3D modeling. We have validated our system, both quantitatively and qualitatively, with four datasets of different object materials. Results show that we are able to generate visually pleasing models for very challenging cases.
Hierarchical Higher-Order Regression Forest Fields: An Application to 3D Indoor Scene Labelling
Authors: Trung T. Pham, Ian Reid, Yasir Latif, Stephen Gould
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: This paper addresses the problem of semantic segmentation of 3D indoor scenes reconstructed from RGB-D images.Traditionally label prediction for 3D points is tackled by employing graphical models that capture scene features and complex relations between different class labels. However, the existing work is restricted to pairwise conditional random fields, which are insufficient when encoding rich scene context. In this work we propose models with higher-order potentials to describe complex relational information from the 3D scenes. Specifically, we relax the labelling problem to a regression, and generalize the higher-order associative P n Potts model to a new family of arbitrary higher-order models based on regression forests. We show that these models, like the robust P n models, can still be decomposed into the sum of pairwise terms by introducing auxiliary variables. Moreover, our proposed higher-order models also permit extension to hierarchical random fields, which allows for the integration of scene context and features computed at different scales. Our potential functions are constructed based on regression forests encoding Gaussian densities that admit efficient inference. The parameters of our model are learned from training data using a structured learning approach. Results on two datasets show clear improvements over current state-of-the-art methods.
Classical Scaling Revisited
Authors: Gil Shamai, Yonathan Aflalo, Michael Zibulevsky, Ron Kimmel
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: Multidimensional-scaling (MDS) is an information analysis tool. It involves the evaluation of distances between data points, which is a quadratic space-time problem. Then, MDS procedures find an embedding of the points in a low dimensional Euclidean (flat) domain, optimizing for the similarity of inter-points distances. We present an efficient solver for Classical Scaling (a specific MDS model) by extending the distances measured from a subset of the points to the rest, while exploiting the smoothness property of the distance functions. The smoothness is measured by the L2 norm of the Laplace-Beltrami operator applied to the unknown distance function. The Laplace Beltrami reflects the local differential relations between points, and can be computed in linear time. Classical-scaling is thereby reformulated into a quasi-linear space-time complexities procedure.
Dense Continuous-Time Tracking and Mapping With Rolling Shutter RGB-D Cameras
Authors: Christian Kerl, Jorg Stuckler, Daniel Cremers
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: We propose a dense continuous-time tracking and mapping method for RGB-D cameras. We parametrize the camera trajectory using continuous B-splines and optimize the trajectory through dense, direct image alignment. Our method also directly models rolling shutter in both RGB and depth images within the optimization, which improves tracking and reconstruction quality for low-cost CMOS sensors.  Using a continuous trajectory representation has a number of advantages over a discrete-time representation (e.g. camera poses at the frame interval). With splines, less variables need to be optimized than with a discrete representation, since the trajectory can be represented with fewer control points than frames. Splines also naturally include smoothness constraints on derivatives of the trajectory estimate. Finally, the continuous trajectory representation allows to compensate for rolling shutter effects, since a pose estimate is available at any exposure time of an image. Our approach demonstrates superior quality in tracking and reconstruction compared to approaches with discrete-time or global shutter assumptions.
Dense Image Registration and Deformable Surface Reconstruction in Presence of Occlusions and Minimal Texture
Authors: Dat Tien Ngo, Sanghyuk Park, Anne Jorstad, Alberto Crivellaro, Chang D. Yoo, Pascal Fua
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: Deformable surface tracking from monocular images is well-known to be under-constrained. Occlusions often make the task even more challenging, and can result in failure if the surface is not sufficiently textured. In this work, we explicitly address the problem of 3D reconstruction of poorly textured, occluded surfaces, proposing a framework based on a template-matching approach that scales dense robust features by a relevancy score. Our approach is extensively compared to current methods employing both local feature matching and dense template alignment. We test on standard datasets as well as on a new dataset (that will be made publicly available) of a sparsely textured, occluded surface. Our framework achieves state-of-the-art results for both well and poorly textured, occluded surfaces.
The Likelihood-Ratio Test and Efficient Robust Estimation
Authors: Andrea Cohen, Christopher Zach
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: Robust estimation of model parameters in the presence of outliers is a key problem in computer vision. RANSAC inspired techniques are widely used in this context, although their application might be limited due to the need of a priori knowledge on the inlier noise level. We propose a new approach for jointly optimizing over model parameters and the inlier noise level based on the likelihood ratio test. This allows control over the type I error incurred. We also propose an early bailout strategy for efficiency. Tests on both synthetic and real data show that our method outperforms the state-of-the-art in a fraction of the time.
Reflection Modeling for Passive Stereo
Authors: Rahul Nair, Andrew Fitzgibbon, Daniel Kondermann, Carsten Rother
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: Stereo reconstruction in presence of reality faces many challenges that still need to be addressed. This paper considers reflections, which introduce incorrect matches due to the observation violating the diffuse-world assumption underlying the majority of stereo techniques. Unlike most existing work, which employ regularization or robust data terms to suppress such errors,  we derive two least squares models from first principles that generalize diffuse world stereo  and explicitly take reflections into account.   These models are parametrized by depth, orientation and material properties, resulting in a total of up to 5 parameters per pixel that have to be estimated.  Additionally large non-local interactions between viewed and reflected surface have to be taken into account.  These two properties make inference of the model appear prohibitive, but we present evidence that inference is actually possible using a variant of patch match stereo.
Detailed Full-Body Reconstructions of Moving People From Monocular RGB-D Sequences
Authors: Federica Bogo, Michael J. Black, Matthew Loper, Javier Romero
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: We accurately estimate the 3D geometry and appearance of the human body from a monocular RGB-D sequence of a user moving freely in front of the sensor. Range data in each frame is first brought into alignment with a multi-resolution 3D body model in a coarse-to-fine process. The method then uses geometry and image texture over time to obtain accurate shape, pose, and appearance information despite unconstrained motion, partial views, varying resolution, occlusion, and soft tissue deformation. Our novel body model has variable shape detail, allowing it to capture faces with a high-resolution deformable head model and body shape with lower-resolution. Finally we combine range data from an entire sequence to estimate a high-resolution displacement map that captures fine shape details. We compare our recovered models with high-resolution scans from a professional system and with avatars created by a commercial product. We extract accurate 3D avatars from challenging motion sequences and even capture soft tissue dynamics.
Efficient Solution to the Epipolar Geometry for Radially Distorted Cameras
Authors: Zuzana Kukelova, Jan Heller, Martin Bujnak, Andrew Fitzgibbon, Tomas Pajdla
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: The estimation of the epipolar geometry of two cameras from image matches is a fundamental problem of computer vision with many applications. While the closely related problem of estimating relative pose of two different uncalibrated cameras with radial distortion is of particular importance, none of the previously published methods is suitable for practical applications. These solutions are either numerically unstable, sensitive to noise, based on a large number of point correspondences, or simply too slow for real-time applications. In this paper, we present a new efficient solution to this problem that uses 10 image correspondences. By manipulating ten input polynomial equations, we derive a degree 10 polynomial equation in one variable. The solutions to this equation are efficiently found using the Sturm sequences method. In the experiments, we show that the proposed solution is stable, noise resistant, and fast, and as such efficiently usable in a practical Structure-from-Motion pipeline.
Learning a Descriptor-Specific 3D Keypoint Detector
Authors: Samuele Salti, Federico Tombari, Riccardo Spezialetti, Luigi Di Stefano
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: Keypoint detection represents the first stage in the majority of modern computer vision pipelines based on automatically established correspondences between local descriptors. However, no standard solution has emerged yet in the case of 3D data such as point clouds or meshes, which exhibit high variability in level of detail and noise. More importantly, existing proposals for 3D keypoint detection rely on geometric saliency functions that attempt to maximize repeatability rather than distinctiveness of the selected regions, which may lead to sub-optimal performance of the overall pipeline. To overcome these shortcomings, we cast 3D keypoint detection as a binary classification between points whose support can be correctly matched by a predefined 3D descriptor or not, thereby learning a descriptor-specific detector that adapts seamlessly to different scenarios. Through  experiments on several public datasets, we show that this novel approach to the design of a keypoint detector represents a flexible solution that, nonetheless, can provide state-of-the-art descriptor matching performance.
Component-Wise Modeling of Articulated Objects
Authors: Valsamis Ntouskos, Marta Sanzari, Bruno Cafaro, Federico Nardi, Fabrizio Natola, Fiora Pirri, Manuel Ruiz
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: We introduce a novel framework for modeling articulated objects based on the aspects of their components. By decomposing the object into components, we divide the problem in smaller modeling tasks. After obtaining 3D models for each component aspect by employing a shape deformation paradigm, we merge them together, forming the object components. The final model is obtained by assembling the components using an optimization scheme which fits the respective 3D models to the corresponding apparent contours in a reference pose.  The results suggest that our approach can produce realistic 3D models of articulated objects in reasonable time.