pwc-archive/papers-with-abstracts huggingface dataset:
The HCI Stereo Metrics: Geometry-Aware Performance Analysis of Stereo Algorithms
Authors: Katrin Honauer, Lena Maier-Hein, Daniel Kondermann
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12

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
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12

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
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12

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
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Point Cloud Completion, Template Matching

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
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12

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
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12

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
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Template Matching

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
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: 4D reconstruction, Object

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
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Translation

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
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: 3D Reconstruction, Object

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
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12

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
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Disparity Estimation, Stereo Matching, Stereo Matching Hand

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
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Stereo Matching, Stereo Matching Hand

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
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Contour Detection, Surface Reconstruction, Transparent objects, valid

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
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: regression, Semantic Segmentation

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
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12

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
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12

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.
The Likelihood-Ratio Test and Efficient Robust Estimation
Authors: Andrea Cohen, Christopher Zach
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12

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
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12

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.