pwc-archive/papers-with-abstracts huggingface dataset:
Self-Calibrating Polarising Radiometric Calibration
Authors: Daniel Teo, Boxin Shi, Yinqiang Zheng, Sai-Kit Yeung
Date: 2018-06-01 00:00:00
Proceeding: CVPR 2018 6

We present a self-calibrating polarising radiometric calibration method. From a set of images taken from a single viewpoint under different unknown polarising angles, we recover the inverse camera response function and the polarising angles relative to the first angle. The problem is solved in an integrated manner, recovering both of the unknowns simultaneously. The method exploits the fact that the intensity of polarised light should vary sinusoidally as the polarising filter is rotated, provided that the response is linear. It offers the first solution to demonstrate the possibility of radiometric calibration through polarisation. We evaluate the accuracy of our proposed method using synthetic data and real world objects captured using different cameras. The self-calibrated results were found to be comparable with those from multiple exposure sequence.
Coding Kendall's Shape Trajectories for 3D Action Recognition
Authors: Amor Ben Tanfous, Hassen Drira, Boulbaba Ben Amor
Date: 2018-06-01 00:00:00
Proceeding: CVPR 2018 6
Tasks: 3D Action Recognition, Action Recognition, Dictionary Learning, Event Detection, General Classification, Temporal Action Localization

Suitable shape representations as well as their temporal evolution, termed trajectories, often lie to non-linear manifolds. This puts an additional constraint (i.e., non-linearity) in using conventional machine learning techniques for the purpose of classification, event detection, prediction, etc. This paper accommodates the well-known Sparse Coding and Dictionary Learning to the Kendall's shape space and illustrates effective coding of 3D skeletal sequences for action recognition. Grounding on the Riemannian geometry of the shape space, an intrinsic sparse coding and dictionary learning formulation is proposed for static skeletal shapes to overcome the inherent non-linearity of the manifold. As a main result, initial trajectories give rise to sparse code functions with suitable computational properties, including sparsity and vector space representation. To achieve action recognition, two different classification schemes were adopted. A bi-directional LSTM is directly performed on sparse code functions, while a linear SVM is applied after representing sparse code functions  using Fourier temporal pyramid. Experiments conducted on three publicly available datasets show the superiority of the proposed approach compared to existing Riemannian representations and its competitiveness with respect to other recently-proposed approaches. When the benefits of invariance are maintained from the Kendall's shape representation, our approach not only overcomes the problem of non-linearity but also yields to discriminative sparse code functions.
Reconstructing Thin Structures of Manifold Surfaces by Integrating Spatial Curves
Authors: Shiwei Li, Yao Yao, Tian Fang, Long Quan
Date: 2018-06-01 00:00:00
Proceeding: CVPR 2018 6
Tasks: Surface Reconstruction

The manifold surface reconstruction in multi-view stereo often fails in retaining thin structures due to incomplete and noisy reconstructed point clouds. In this paper, we address this problem by leveraging spatial curves. The curve representation in nature is advantageous in modeling thin and elongated structures, implying topology and connectivity information of the underlying geometry, which exactly compensates the weakness of scattered point clouds. We present a novel surface reconstruction method using both curves and point clouds. First, we propose a 3D curve reconstruction algorithm based on the initialize-optimize-expand strategy. Then, tetrahedra are constructed from points and curves, where the volumes of thin structures are robustly preserved by the Curve-conformed Delaunay Refinement. Finally, the mesh surface is extracted from tetrahedra by a graph optimization. The method has been intensively evaluated on both synthetic and real-world datasets, showing significant improvements over state-of-the-art methods.
Deep Marching Cubes: Learning Explicit Surface Representations
Authors: Yiyi Liao, Simon DonnÃ©, Andreas Geiger
Date: 2018-06-01 00:00:00
Proceeding: CVPR 2018 6

Existing learning based solutions to 3D surface prediction cannot be trained end-to-end as they operate on intermediate representations (e.g., TSDF) from which 3D surface meshes must be extracted in a post-processing step (e.g., via the marching cubes algorithm). In this paper, we investigate the problem of end-to-end 3D surface prediction. We first demonstrate that the marching cubes algorithm is not differentiable and propose an alternative differentiable formulation which we insert as a final layer into a 3D convolutional neural network. We further propose a set of loss functions which allow for training our model with sparse point supervision. Our experiments demonstrate that the model allows for predicting sub-voxel accurate 3D shapes of arbitrary topology. Additionally, it learns to complete shapes and to separate an object's inside from its outside even in the presence of sparse and incomplete ground truth. We investigate the benefits of our approach on the task of inferring shapes from 3D point clouds. Our model is flexible and can be combined with a variety of shape encoder and shape inference techniques.
Uncalibrated Photometric Stereo Under Natural Illumination
Authors: Zhipeng Mo, Boxin Shi, Feng Lu, Sai-Kit Yeung, Yasuyuki Matsushita
Date: 2018-06-01 00:00:00
Proceeding: CVPR 2018 6

This paper presents a photometric stereo method that works with unknown natural illuminations without any calibration object. To solve this challenging problem, we propose the use of an equivalent directional lighting model for small surface patches consisting of slowly varying normals, and solve each patch up to an arbitrary rotation ambiguity. Our method connects the resulting patches and unifies the local ambiguities to a global rotation one through angular distance propagation defined over the whole surface. After applying the integrability constraint, our final solution contains only a binary ambiguity, which could be easily removed. Experiments using both synthetic and real-world datasets show our method provides even comparable results to calibrated methods
Free Supervision From Video Games
Authors: Philipp KrÃ¤henbÃ¼hl
Date: 2018-06-01 00:00:00
Proceeding: CVPR 2018 6
Tasks: Depth Estimation, Instance Segmentation, Intrinsic Image Decomposition, Optical Flow Estimation, Semantic Segmentation

Deep networks are extremely hungry for data. They devour hundreds of thousands of labeled images to learn robust and semantically meaningful feature representations. Current networks are so data hungry that collecting labeled data has become as important as designing the networks themselves. Unfortunately, manual data collection is both expensive and time consuming. We present an alternative, and show how ground truth labels for many vision tasks are easily extracted from video games in real time as we play them. We interface the popular Microsoft DirectX rendering API, and inject specialized rendering code into the game as it is running. This code produces ground truth labels for instance segmentation, semantic labeling, depth estimation, optical flow, intrinsic image decomposition, and instance tracking. Instead of labeling images, a researcher now simply plays video games all day long. Our method is general and works on a wide range of video games. We collected a dataset of 220k training images, and 60k test images across 3 video games, and evaluate state of the art optical flow, depth estimation and intrinsic image decomposition algorithms. Our video game data is visually closer to real world images, than other synthetic dataset.
Planar Shape Detection at Structural Scales
Authors: Hao Fang, Florent Lafarge, Mathieu Desbrun
Date: 2018-06-01 00:00:00
Proceeding: CVPR 2018 6

Interpreting 3D data such as point clouds or surface meshes depends heavily on the scale of observation. Yet, existing algorithms for shape detection rely on trial-and-error parameter tunings to output configurations representative of a structural scale. We present a framework to automatically extract a set of representations that capture the shape and structure of man-made objects at different key abstraction levels. A shape-collapsing process first generates a fine-to-coarse sequence of shape representations by exploiting local planarity. This sequence is then analyzed to identify significant geometric variations between successive representations through a supervised energy minimization. Our framework is flexible enough to learn how to detect both existing structural formalisms such as the CityGML Levels Of Details, and expert-specified levels of abstraction. Experiments on different input data and classes of man-made objects, as well as comparisons with existing shape detection methods, illustrate the strengths of our approach in terms of efficiency and flexibility.
Camera Pose Estimation With Unknown Principal Point
Authors: Viktor Larsson, Zuzana Kukelova, Yinqiang Zheng
Date: 2018-06-01 00:00:00
Proceeding: CVPR 2018 6
Tasks: Camera Localization, Camera Pose Estimation, Pose Estimation

To estimate the 6-DoF extrinsic pose of a pinhole camera with partially unknown intrinsic parameters is a critical sub-problem in structure-from-motion and camera localization. In most of existing camera pose estimation solvers, the principal point is assumed to be in the image center. Unfortunately, this assumption is not always true, especially for asymmetrically cropped images. In this paper, we develop the first exactly minimal solver for the case of unknown principal point and focal length by using four and a half point correspondences (P4.5Pfuv). We also present an extremely fast solver for the case of unknown aspect ratio (P5Pfuva). The new solvers outperform the previous state-of-the-art in terms of stability and speed. Finally, we explore the extremely challenging case of both unknown principal point and radial distortion, and develop the first practical non-minimal solver by using seven point correspondences (P7Pfruv). Experimental results on both simulated data and real Internet images demonstrate the usefulness of our new solvers.
Inverse Composition Discriminative Optimization for Point Cloud Registration
Authors: Jayakorn Vongkulbhisal, BeÃ±at Irastorza Ugalde, Fernando de la Torre, JoÃ£o P. Costeira
Date: 2018-06-01 00:00:00
Proceeding: CVPR 2018 6
Tasks: Point Cloud Registration

Rigid Point Cloud Registration (PCReg) refers to the problem of finding the rigid transformation between two sets of point clouds. This problem is particularly important due to the advances in new 3D sensing hardware, and it is challenging because neither the correspondence nor the transformation parameters are known. Traditional local PCReg methods (e.g., ICP) rely on local optimization algorithms, which can get trapped in bad local minima in the presence of noise, outliers, bad initializations, etc. To alleviate these issues, this paper proposes Inverse Composition Discriminative Optimization (ICDO), an extension of Discriminative Optimization (DO), which learns a sequence of update steps from synthetic training data that search the parameter space for an improved solution. Unlike DO, ICDO is object-independent and generalizes even to unseen shapes. We evaluated ICDO on both synthetic and real data, and show that ICDO can match the speed and outperform the accuracy of state-of-the-art PCReg algorithms.
A Fast Resection-Intersection Method for the Known Rotation Problem
Authors: Qianggong Zhang, Tat-Jun Chin, Huu Minh Le
Date: 2018-06-01 00:00:00
Proceeding: CVPR 2018 6
Tasks: Pose Estimation

The known rotation problem refers to a special case of structure-from-motion where the absolute orientations of the cameras are known. When formulated as a minimax (l_infty) problem on reprojection errors, the problem is an instance of pseudo-convex programming. Though theoretically tractable, solving the known rotation problem on large-scale data (1,000âs of views, 10,000âs scene points) using existing methods can be very time-consuming. In this paper, we devise a fast algorithm for the known rotation problem. Our approach alternates between pose estimation and triangulation (i.e., resection-intersection) to break the problem into multiple simpler instances of pseudo-convex programming. The key to the vastly superior performance of our method lies in using a novel minimum enclosing ball (MEB) technique for the calculation of updating steps, which obviates the need for convex optimisation routines and greatly reduces memory footprint. We demonstrate the practicality of our method on large-scale problem instances which easily overwhelm current state-of-the-art algorithms (demo program available in supplementary).
Learning Patch Reconstructability for Accelerating Multi-View Stereo
Authors: Alex Poms, Chenglei Wu, Shoou-I Yu, Yaser Sheikh
Date: 2018-06-01 00:00:00
Proceeding: CVPR 2018 6
Tasks: Stereo Matching, Stereo Matching Hand, Surface Reconstruction

We present an approach to accelerate multi-view stereo (MVS) by prioritizing computation on image patches that are likely to produce accurate 3D surface reconstructions. Our key insight is that the accuracy of the surface reconstruction from a given image patch can be predicted significantly faster than performing the actual stereo matching. The intuition is that non-specular, fronto-parallel, in-focus patches are more likely to produce accurate surface reconstructions than highly specular, slanted, blurry patches --- and that these properties can be reliably predicted from the image itself. By prioritizing stereo matching on a subset of patches that are highly reconstructable and also cover the 3D surface, we are able to accelerate MVS with minimal reduction in accuracy and completeness. To predict the reconstructability score of an image patch from a single view, we train an image-to-reconstructability neural network: the I2RNet. This reconstructability score enables us to efficiently identify image patches that are likely to provide the most accurate surface estimates before performing stereo matching. We demonstrate that the I2RNet, when trained on the ScanNet dataset, generalizes to the DTU and Tanks and Temples MVS datasets. By using our I2RNet with an existing MVS implementation, we show that our method can achieve more than a 30x speed-up over the baseline with only an minimal loss in completeness.
Progressively Complementarity-Aware Fusion Network for RGB-D Salient Object Detection
Authors: Hao Chen, Youfu Li
Date: 2018-06-01 00:00:00
Proceeding: CVPR 2018 6
Tasks: object-detection, Object Detection, RGB-D Salient Object Detection, RGB Salient Object Detection, Salient Object Detection

How to incorporate cross-modal complementarity sufficiently is the cornerstone question for RGB-D salient object detection. Previous works mainly address this issue by simply concatenating multi-modal features or combining unimodal predictions. In this paper, we answer this question from two perspectives: (1) We argue that if the complementary part can be modelled more explicitly, the cross-modal complement is likely to be better captured. To this end, we design a novel complementarity-aware fusion (CA-Fuse) module when adopting the Convolutional Neural Network (CNN). By introducing cross-modal residual functions and complementarity-aware supervisions in each CA-Fuse module, the problem of learning complementary information from the paired modality is explicitly posed as asymptotically approximating the residual function. (2) Exploring the complement across all the levels. By cascading the CA-Fuse module and adding level-wise supervision from deep to shallow densely, the cross-level complement can be selected and combined progressively. The proposed RGB-D fusion network disambiguates both cross-modal and cross-level fusion processes and enables more sufficient fusion results. The experiments on public datasets show the effectiveness of the proposed CA-Fuse module and the RGB-D salient object detection network.
Defocus Blur Detection via Multi-Stream Bottom-Top-Bottom Fully Convolutional Network
Authors: Wenda Zhao, Fan Zhao, Dong Wang, Huchuan Lu
Date: 2018-06-01 00:00:00
Proceeding: CVPR 2018 6
Tasks: Defocus Blur Detection, Defocus Estimation

Defocus blur detection (DBD) is the separation of infocus and out-of-focus regions in an image. This process has been paid considerable attention because of its remarkable potential applications. Accurate differentiation of homogeneous regions and detection of low-contrast focal regions, as well as suppression of background clutter, are challenges associated with DBD. To address these issues, we propose a multi-stream bottom-top-bottom fully convolutional network (BTBNet), which is the first attempt to develop an end-to-end deep network for DBD. First, we develop a fully convolutional BTBNet to integrate low-level cues and high-level semantic information. Then, considering that the degree of defocus blur is sensitive to scales, we propose multi-stream BTBNets that handle input images with different scales to improve the performance of DBD. Finally, we design a fusion and recursive reconstruction network to recursively refine the preceding blur detection maps. To promote further study and evaluation of the DBD models, we construct a new database of 500 challenging images and their pixel-wise defocus blur annotations. Experimental results on the existing and our new datasets demonstrate that the proposed method achieves significantly better performance than other state-of-the-art algorithms.
âZero-Shotâ Super-Resolution Using Deep Internal Learning
Authors: Assaf Shocher, Nadav Cohen, Michal Irani
Date: 2018-06-01 00:00:00
Proceeding: CVPR 2018 6
Tasks: Image Compression, Super-Resolution

Deep Learning has led to a dramatic leap in Super-Resolution (SR) performance in the past few years. However, being supervised, these SR methods are restricted to specific training data, where the acquisition of the low-resolution (LR) images from their high-resolution (HR) counterparts is predetermined (e.g., bicubic downscaling), without any distracting artifacts (e.g., sensor noise, image compression, non-ideal PSF, etc). Real LR images, however, rarely obey these restrictions, resulting in poor SR results by SotA (State of the Art) methods. In this paper we introduce ``Zero-Shot'' SR, which exploits the power of Deep Learning, but does not rely on prior training. We exploit the internal recurrence of information inside a single image, and train a small image-specific CNN at test time, on examples extracted solely from the input image itself. As such, it can adapt itself to different settings per image. This allows to perform SR of real old photos, noisy images, biological data, and other images where the acquisition process is unknown or non-ideal. On such images, our method outperforms SotA  CNN-based SR methods, as well as previous unsupervised SR methods. To the best of our knowledge, this is the first unsupervised CNN-based SR method.
Detect Globally, Refine Locally: A Novel Approach to Saliency Detection
Authors: Tiantian Wang, Lihe Zhang, Shuo Wang, Huchuan Lu, Gang Yang, Xiang Ruan, Ali Borji
Date: 2018-06-01 00:00:00
Proceeding: CVPR 2018 6
Tasks: object-detection, Object Detection, RGB Salient Object Detection, Saliency Detection, Salient Object Detection

Effective integration of contextual information is crucial for salient object detection. To achieve this, most existing methods based on 'skip' architecture mainly focus on how to integrate hierarchical features of Convolutional Neural Networks (CNNs). They simply apply concatenation or element-wise operation to incorporate high-level semantic cues and low-level detailed information. However, this can degrade the quality of predictions because cluttered and noisy information can also be passed through. To address this problem, we proposes a global Recurrent Localization Network (RLN) which exploits contextual information by the weighted response map in order to localize salient objects more accurately. % and emphasize more on useful ones. Particularly, a recurrent module is employed to progressively refine the inner structure of the CNN over multiple time steps. Moreover, to effectively recover object boundaries, we propose a local Boundary Refinement Network (BRN) to adaptively learn the local contextual information for each spatial position. The learned propagation coefficients can be used to optimally capture relations between each pixel and its neighbors. Experiments on five challenging datasets show that our approach performs favorably against all existing methods in terms of the popular evaluation metrics.
KIPPI: KInetic Polygonal Partitioning of Images
Authors: Jean-Philippe Bauchet, Florent Lafarge
Date: 2018-06-01 00:00:00
Proceeding: CVPR 2018 6
Tasks: Superpixels

Recent works showed that floating polygons can be an interesting alternative to traditional superpixels, especially for analyzing scenes with strong geometric signatures, as man-made environments. Existing algorithms produce homogeneously-sized polygons that fail to capture thin geometric structures and over-partition large uniform areas. We propose a kinetic approach that brings more flexibility on polygon shape and size. The key idea consists in progressively extending pre-detected line-segments until they meet each other. Our experiments demonstrate that output partitions both contain less polygons and better capture geometric structures than those delivered by existing methods. We also show the applicative potential of the method when used as preprocessing in object contouring.
Image Blind Denoising With Generative Adversarial Network Based Noise Modeling
Authors: Jingwen Chen, Jia-Wei Chen, Hongyang Chao, Ming Yang
Date: 2018-06-01 00:00:00
Proceeding: CVPR 2018 6
Tasks: Denoising, Generative Adversarial Network

In this paper, we consider a typical image blind denoising problem, which is to remove unknown noise from noisy images. As we all know, discriminative learning based methods, such as DnCNN, can achieve state-of-the-art denoising results, but they are not applicable to this problem due to the lack of paired training data. To tackle the barrier, we propose a novel two-step framework. First, a Generative Adversarial Network (GAN) is trained to estimate the noise distribution over the input noisy images and to generate noise samples. Second, the noise patches sampled from the first step are utilized to construct a paired training dataset, which is used, in turn, to train a deep Convolutional Neural Network (CNN) for denoising. Extensive experiments have been done to demonstrate the superiority of our approach in image blind denoising.