pwc-archive/papers-with-abstracts huggingface dataset:
3D Reconstruction of Transparent Objects With Position-Normal Consistency
Authors: Yiming Qian, Minglun Gong, Yee Hong Yang
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: 3D Reconstruction, Position, Transparent objects

Estimating the shape of transparent and refractive objects is one of the few open problems in 3D reconstruction. Under the assumption that the rays refract only twice when traveling through the object, we present the first approach to simultaneously reconstructing the 3D positions and normals of the object's surface at both refraction locations. Our acquisition setup requires only two cameras and one monitor, which serves as the light source. After acquiring the ray-ray correspondences between each camera and the monitor, we solve an optimization function which enforces a new position-normal consistency constraint. That is, the 3D positions of surface points shall agree with the normals required to refract the rays under Snell's law. Experimental results using both synthetic and real data demonstrate the robustness and accuracy of the proposed approach.
Recovering Transparent Shape From Time-Of-Flight Distortion
Authors: Kenichiro Tanaka, Yasuhiro Mukaigawa, Hiroyuki Kubo, Yasuyuki Matsushita, Yasushi Yagi
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Object

This paper presents a method for recovering shape and normal of a transparent object from a single viewpoint using a Time-of-Flight (ToF) camera. Our method is built upon the fact that the speed of light varies with the refractive index of the medium and therefore the depth measurement of a transparent object with a ToF camera may be distorted. We show that, from this ToF distortion, the refractive light path can be uniquely determined by estimating a single parameter. We estimate this parameter by introducing a surface normal consistency between the one determined by a light path candidate and the other computed from the corresponding shape. The proposed method is evaluated by both simulation and real-world experiments and shows faithful transparent shape recovery.
Robust Light Field Depth Estimation for Noisy Scene With Occlusion
Authors: W. Williem, In Kyu Park
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Depth Estimation

Light field depth estimation is an essential part of many light field applications. Numerous algorithms have been developed using various light field characteristics. However, conventional methods fail when handling noisy scene with occlusion. To remedy this problem, we present a light field depth estimation method which is more robust to occlusion and less sensitive to noise. Novel data costs using angular entropy metric and adaptive defocus response are introduced. Integration of both data costs improves the occlusion and noise invariant capability significantly. Cost volume filtering and graph cut optimization are utilized to improve the accuracy of the depth map. Experimental results confirm that the proposed method is robust and achieves high quality depth maps in various scenes. The proposed method outperforms the state-of-the-art light field depth estimation methods in qualitative and quantitative evaluation.
Rotational Crossed-Slit Light Field
Authors: Nianyi Li, Haiting Lin, Bilin Sun, Mingyuan Zhou, Jingyi Yu
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Stereo Matching, Stereo Matching Hand

   Light fields (LFs) are image-based representation that records the radiance along all rays along every direction through every point in space. Traditionally LFs are acquired by using a 2D grid of evenly spaced pinhole cameras or by translating a pinhole camera along the 2D grid using a robot arm. In this paper, we present a novel LF sampling scheme by exploiting a special non-centric camera called the crossed-slit or XSlit camera. An XSlit camera acquires rays that simultaneously pass through two oblique slits. We show that, instead of translating the camera as in the pinhole case, we can effectively sample the LF by rotating individual or both slits while keeping the camera fixed. This leads a "fixed-location" LF acquisition scheme. We further show through theoretical analysis and experiments that the resulting XSlit LFs provide several advantages: they provide more dense spatial-angular sampling, are amenable multi-view stereo matching and volumetric reconstruction, and can synthesize unique refocusing effects.
Single Image Object Modeling Based on BRDF and R-Surfaces Learning
Authors: Fabrizio Natola, Valsamis Ntouskos, Fiora Pirri, Marta Sanzari
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Object

A methodology for 3D surface modeling from a single image is proposed.  The principal novelty is concave and specular surface modeling without any externally imposed prior. The main idea of the method is to use BRDFs and generated rendered surfaces, to transfer the normal field, computed for the generated samples, to the unknown surface.  The transferred information is adequate to blow and sculpt the segmented image mask in to a bas-relief of the object. The object surface is further refined basing on a  photo-consistency formulation that relates for error minimization the original image and the modeled object.
A Nonlinear Regression Technique for Manifold Valued Data With Applications to Medical Image Analysis
Authors: Monami Banerjee, Rudrasis Chakraborty, Edward Ofori, Michael S. Okun, David E. Viallancourt, Baba C. Vemuri
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Medical Image Analysis, regression

Regression is an essential tool in Statistical analysis of data with many applications in Computer Vision, Machine Learning, Medical Imaging and various disciplines of Science and Engineering. Linear and nonlinear regression in a vector space setting has been well studied in literature. However, generalizations to manifold-valued data are only recently gaining popularity. With the exception of a few, most existing methods of regression for manifold valued data are limited to geodesic regression which is a generalization of the linear regression in vector-spaces. In this paper, we present a novel nonlinear kernel-based regression method that is applicable to manifold valued data. Our method is applicable to cases when the independent and dependent variables in the regression model are both manifold-valued or one is manifold-valued and the other is vector or scalar valued. Further, unlike most methods, our method does not require any imposed ordering on the manifold-valued data. The performance of our model is tested on a large number of real data sets acquired from Alzhiemers and movement disorder (Parkinsons and Essential Tremor) patients. We present an extensive set of results along with statistical validation and comparisons.
RAID-G: Robust Estimation of Approximate Infinite Dimensional Gaussian With Application to Material Recognition
Authors: Qilong Wang, Peihua Li, WangMeng Zuo, Lei Zhang
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Material Recognition

Infinite dimensional covariance descriptors can provide richer and more discriminative information than their low dimensional counterparts. In this paper, we propose a novel image descriptor, namely, robust approximate infinite dimensional Gaussian (RAID-G). The challenges of RAID-G mainly lie on two aspects: (1) description of infinite dimensional Gaussian is difficult due to its non-linear Riemannian geometric structure and the infinite dimensional setting, hence effective approximation is necessary; (2) traditional maximum likelihood estimation (MLE) is not robust to high (even infinite) dimensional covariance matrix in Gaussian setting. To address these challenges, explicit feature mapping (EFM) is first introduced for effective approximation of infinite dimensional Gaussian induced by additive kernel function, and then a new regularized MLE method based on von Neumann divergence is proposed for robust estimation of covariance matrix. The EFM and proposed regularized MLE allow a closed-form of RAID-G, which is very efficient and effective for high dimensional features. We extend RAID-G by using the outputs of deep convolutional neural networks as original features, and apply it to material recognition. Our approach is evaluated on five material benchmarks and one fine-grained benchmark. It achieves 84.9% accuracy on FMD and 86.3% accuracy on UIUC material database, which are much higher than state-of-the-arts.
Logistic Boosting Regression for Label Distribution Learning
Authors: Chao Xing, Xin Geng, Hui Xue
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Age Estimation, Facial Expression Recognition, Facial Expression Recognition (FER), Multi-Label Learning, regression

Label Distribution Learning (LDL) is a general learning framework which includes both single label and multi-label learning as its special cases. One of the main assumptions made in traditional LDL algorithms is the derivation of the parametric model as the maximum entropy model. While it is a reasonable assumption without additional information, there is no particular evidence supporting it in the problem of LDL. Alternatively, using a general LDL model family to approximate this parametric model can avoid the potential influence of the specific model. In order to learn this general model family, this paper uses a method called Logistic Boosting Regression (LogitBoost) which can be seen as an additive weighted function regression from the statistical viewpoint. For each step, we can fit individual weighted regression function (base learner) to realize the optimization gradually. The base learners are chosen as weighted regression tree and vector tree, which constitute two algorithms named LDLogitBoost and AOSO-LDLogitBoost in this paper. Experiments on facial expression recognition, crowd opinion prediction on movies and apparent age estimation show that LDLogitBoost and AOSO-LDLogitBoost can achieve better performance than traditional LDL algorithms as well as other LogitBoost algorithms.
Efficient Temporal Sequence Comparison and Classification Using Gram Matrix Embeddings on a Riemannian Manifold
Authors: Xikang Zhang, Yin Wang, Mengran Gou, Mario Sznaier, Octavia Camps
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: 3D Action Recognition, Action Recognition, General Classification, Temporal Action Localization, Temporal Sequences

In this paper we propose a new framework to compare and classify temporal sequences. The proposed approach captures the underlying dynamics of the data while avoiding expensive estimation procedures, making it suitable to process large numbers of sequences. The main idea is to first embed the  sequences into  a Riemannian manifold by  using positive definite regularized Gram matrices of  their Hankelets.  The  advantages of the this approach are: 1) it allows for using non-Euclidean similarity functions  on the Positive Definite matrix manifold, which capture better the underlying geometry than directly comparing the sequences or their  Hankel matrices; and 2)  Gram matrices inherit desirable properties from the underlying Hankel matrices: their rank  measure the complexity of the underlying dynamics, and the rank and the coefficients of the associated regressive models are  invariant to affine transformations and varying initial conditions. The benefits of this approach are illustrated with extensive experiments in 3D action recognition using 3D joints sequences. In spite of its simplicity,  the  performance of this approach is competitive or  better  than  using state-of-art approaches for this problem.  Further, these results hold across a variety of  metrics, supporting the idea that the improvement stems from the embedding itself, rather than from  using  one of these metrics.
Semantic Filtering
Authors: Qingxiong Yang
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Contour Detection

Edge-preserving image operations aim at smoothing an image without blurring the edges. Many excellent edge-preserving filtering techniques have been proposed recently to reduce the computational complexity or/and separate different scale structures. They normally adopt a user-selected scale measurement to control the detail/texture smoothing. However, natural photos contain objects of different sizes which cannot be described by a single scale measurement. On the other hand, edge/contour detection/analysis is closely related to edge-preserving filtering and has achieved significant progress recently. Nevertheless, most of the state-of-the-art filtering techniques ignore the success in this area. Inspired by the fact that learning-based edge detectors/classifiers significantly outperform traditional manually-designed detectors, this paper proposes a learning-based edge-preserving filtering technique. It synergistically combines the efficiency of the recursive filter and the effectiveness of the recent edge detector for scale-aware edge-preserving filtering. Unlike previous filtering methods, the propose filter can efficiently extract subjectively-meaningful structures from natural scenes containing multiple-scale objects.
UAV Sensor Fusion With Latent-Dynamic Conditional Random Fields in Coronal Plane Estimation
Authors: Amir M. Rahimi, Raphael Ruschel, B. S. Manjunath
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Sensor Fusion

We present a real-time body orientation estimation in a micro-Unmanned Air Vehicle video stream. This work is part of a fully autonomous UAV system which can maneuver to face a single individual in challenging outdoor environments. Our body orientation estimation consists of the following steps: (a) obtaining a set of visual appearance models for each body orientation, where each model is tagged with a set of scene information (obtained from sensors); (b) exploiting the mutual information of on-board sensors using latent-dynamic conditional random fields (LDCRF); (c) Characterizing each visual appearance model with the most discriminative sensor information; (d) fast estimation of body orientation during the test flights given the LDCRF parameters and the corresponding sensor readings. The key aspects of our approach is to add sparsity to the sensor readings with latent variables followed by long range dependency analysis. Experimental results obtained over real-time video streams demonstrate a significant improvement in both speed (15-fps) and accuracy (72%) compared to the state of the art techniques that only rely on visual data. Video demonstration of our autonomous flights (both from ground view and aerial view) are included in the supplementary material.
Robust Visual Place Recognition With Graph Kernels
Authors: Elena Stumm, Christopher Mei, Simon Lacroix, Juan Nieto, Marco Hutter, Roland Siegwart
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Visual Place Recognition

A novel method for visual place recognition is introduced and evaluated, demonstrating robustness to perceptual aliasing and observation noise. This is achieved by increasing discrimination through a more structured representation of visual observations. Estimation of observation likelihoods are based on graph kernel formulations, utilizing both the structural and visual information encoded in covisibility graphs. The proposed probabilistic model is able to circumvent the typically difficult and expensive posterior normalization procedure by exploiting the information available in visual observations. Furthermore, the place recognition complexity is independent of the size of the map. Results show improvements over the state-of-the-art on a diverse set of both public datasets and novel experiments, highlighting the benefit of the approach.
Consensus of Non-Rigid Reconstructions
Authors: Minsik Lee, Jungchan Cho, Songhwai Oh
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6

Recently, there have been many progresses for the problem of non-rigid structure reconstruction based on 2D trajectories, but it is still challenging to deal with complex deformations or restricted view ranges. Promising alternatives are the piecewise reconstruction approaches, which divide trajectories into several local parts and stitch their individual reconstructions to produce an entire 3D structure. These methods show the state-of-the-art performance, however, most of them are specialized for relatively smooth surfaces and some are quite complicated. Meanwhile, it has been reported numerously in the field of pattern recognition that obtaining consensus from many weak hypotheses can give a strong, powerful result. Inspired by these reports, in this paper, we push the concept of part-based reconstruction to the limit: Instead of considering the parts as explicitly-divided local patches, we draw a large number of small random trajectory sets. From their individual reconstructions, we pull out a statistic of each 3D point to retrieve a strong reconstruction, of which the procedure can be expressed as a sparse l_1-norm minimization problem. In order to resolve the reflection ambiguity between weak (and possibly bad) reconstructions, we propose a novel optimization framework which only involves a single eigenvalue decomposition. The proposed method can be applied to any type of data and outperforms the existing methods for the benchmark sequences, even though it is composed of a few, simple steps. Furthermore, it is easily parallelizable, which is another advantage.
Isometric Non-Rigid Shape-From-Motion in Linear Time
Authors: Shaifali Parashar, Daniel Pizarro, Adrien Bartoli
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6

We study Isometric Non-Rigid Shape-from-Motion (Iso-NRSfM): given multiple intrinsically calibrated monocular images, we want to reconstruct the time-varying 3D shape of an object undergoing isometric deformations.   We show that Iso-NRSfM is solvable from the warps (the inter-image geometric transformations).   We propose a new theoretical framework based on Riemmanian manifolds to represent the unknown 3D surfaces, as embeddings of the camera's retinal planes.   This allows us to use the manifolds' metric tensor and Christoffel Symbol fields, which we prove are related across images by simple rules depending only on the warps.   This forms a set of important theoretical results.   Using the infinitesimal planarity formulation, it then allows us to derive a system of two quartics in two variables for each image pair.   The sum-of-squares of these polynomials is independent of the number of images and can be solved globally, forming a well-posed problem for N >= 3 images, whose solution directly leads to the surface's normal field.   The proposed method outperforms existing work in terms of accuracy and computation cost on synthetic and real datasets.
Learning Online Smooth Predictors for Realtime Camera Planning Using Recurrent Decision Trees
Authors: Jianhui Chen, Hoang M. Le, Peter Carr, Yisong Yue, James J. Little
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6

We study the problem of online prediction for realtime camera planning, where the goal is to predict smooth trajectories that correctly track and frame objects of interest (e.g., players in a basketball game). The conventional approach for training predictors does not directly consider temporal consistency, and often produces undesirable jitter. Although post-hoc smoothing (e.g., via a Kalman filter) can mitigate this issue to some degree, it is not ideal due to overly stringent modeling assumptions (e.g., Gaussian noise). We propose a recurrent decision tree framework that can directly incorporate temporal consistency into a data-driven predictor, as well as a learning algorithm that can efficiently learn such temporally smooth models. Our approach does not require any post-processing, making online smooth predictions much easier to generate when the noise model is unknown. We apply our approach to sports broadcasting: given noisy player detections, we learn where the camera should look based on human demonstrations. Our experiments exhibit significant improvements over conventional baselines and showcase the practicality of our approach.
Egocentric Future Localization
Authors: Hyun Soo Park, Jyh-Jing Hwang, Yedong Niu, Jianbo Shi
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Motion Planning

We presents a method for future localization: to predict plausible future trajectories of ego-motion in egocentric stereo images.  Our paths avoid obstacles, move between objects, even turn around a corner into space behind objects. As a byproduct of the predicted trajectories, we discover the empty space occluded by foreground objects.   One key innovation is the creation of an EgoRetinal map, akin to an illustrated tourist map, that `rearranges' pixels taking into accounts depth information, the ground plane, and body motion direction, so that it allows motion planning and perception of objects on one image space. We learn to plan trajectories  directly on this EgoRetinal map using first person experience of walking around in a variety of scenes.  In a testing phase, given an novel scene, we find multiple hypotheses of future trajectories from the learned experience.  We refine them by minimizing a cost function that describes compatibility between the obstacles in the EgoRetinal map and trajectories.  We quantitatively evaluate our method to show predictive validity and apply to various real world daily activities including walking, shopping, and social interactions.
WELDON: Weakly Supervised Learning of Deep Convolutional Neural Networks
Authors: Thibaut Durand, Nicolas Thome, Matthieu Cord
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6
Tasks: Multiple Instance Learning, Weakly-supervised Learning

In this paper, we introduce a novel framework for WEakly supervised Learning of Deep cOnvolutional neural Networks (WELDON). Our method is dedicated to automatically selecting relevant image regions from weak annotations, e.g. global image labels, and encompasses the following contributions. Firstly, WELDON leverages recent improvements on the Multiple Instance Learning paradigm,  i.e. negative evidence scoring and top instance selection. Secondly, the deep CNN is trained to optimize Average Precision, and fine-tuned on the target dataset with efficient computations due to convolutional feature sharing. A thorough experimental validation shows that WELDON outperforms state-of-the-art results on six different datasets.
Event-Specific Image Importance
Authors: Yufei Wang, Zhe Lin, Xiaohui Shen, Radomir Mech, Gavin Miller, Garrison W. Cottrell
Date: 2016-06-01 00:00:00
Proceeding: CVPR 2016 6

When creating a photo album of an event, people typically select a few important images to keep or share. There is some consistency in the process of choosing the important images, and discarding the unimportant ones. Modeling this selection process will assist automatic photo selection and album summarization. In this paper, we show that the selection of important images is consistent among different viewers, and that this selection process is related to the event type of the album. We introduce the concept of event-specific image importance. We collected a new event album dataset with human annotation of the relative image importance with each event album. We also propose a Convolutional Neural Network (CNN) based method to predict the image importance score of a given event album, using a novel rank loss function and a progressive training scheme. Results demonstrate that our method significantly outperforms various baseline methods.