Performance Analysis and Robustification of Single-query 6-DoF Camera Pose Estimation

We consider a single-query 6-DoF camera pose estimation with reference images and a point cloud, i.e. the problem of estimating the position and orientation of a camera by using reference images and a point cloud. In this work, we perform a systematic comparison of three state-of-the-art strategies for 6-DoF camera pose estimation, i.e. feature-based, photometric-based and mutual-information-based approaches. The performance of the studied methods is evaluated on two standard datasets in terms of success rate, translation error and max orientation error. Building on the results analysis, we propose a hybrid approach that combines feature-based and mutual-information-based pose estimation methods since it provides complementary properties for pose estimation. Experiments show that (1) in cases with large environmental variance, the hybrid approach outperforms feature-based and mutual-information-based approaches by an average of 25.1% and 5.8% in terms of success rate, respectively; (2) in cases where query and reference images are captured at similar imaging conditions, the hybrid approach performs similarly as the feature-based approach, but outperforms both photometric-based and mutual-information-based approaches with a clear margin; (3) the feature-based approach is consistently more accurate than mutual-information-based and photometric-based approaches when at least 4 consistent matching points are found between the query and reference images.

Dichromatic Gray Pixel for Camera-agnostic Color Constancy

We present a statistical color constancy method that relies on novel gray pixel detection and mean shift clustering. The method, called Mean Shifted Grey Pixel -- MSGP -- is compact, easy to compute and requires no training. Experiments on different datasets show that the proposed approach outperforms state-of-the-art methods in the camera-agnostic scenario. In the setting where the camera is known, MSGP outperforms all statistical methods.

Fast Motion Deblurring for Feature Detection and Matching Using Inertial Measurements

Many computer vision and image processing applications rely on local features. It is well-known that motion blur decreases the performance of traditional feature detectors and descriptors. We propose an inertial-based deblurring method for improving the robustness of existing feature detectors and descriptors against the motion blur. Unlike most deblurring algorithms, the method can handle spatially-variant blur and rolling shutter distortion. Furthermore, it is capable of running in real-time contrary to state-of-the-art algorithms. The limitations of inertial-based blur estimation are taken into account by validating the blur estimates using image data. The evaluation shows that when the method is used with traditional feature detector and descriptor, it increases the number of detected keypoints, provides higher repeatability and improves the localization accuracy. We also demonstrate that such features will lead to more accurate and complete reconstructions when used in the application of 3D visual reconstruction.

Improving CNN classifiers by estimating test-time priors

The problem of different training and test set class priors is addressed in the context of CNN classifiers. An EM-based algorithm for test-time class priors estimation is evaluated on fine-grained computer vision problems for both the batch and on-line situations. Experimental results show a significant improvement on the fine-grained classification tasks using the known evaluation-time priors, increasing the top-1 accuracy by 4.0% on the FGVC iNaturalist 2018 validation set and by 3.9% on the FGVCx Fungi 2018 validation set. Iterative estimation of test-time priors on the PlantCLEF 2017 dataset increased the image classification accuracy by 3.4%, allowing a single CNN model to achieve state-of-the-art results and outperform the competition-winning ensemble of 12 CNNs.

DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks

We present DeblurGAN, an end-to-end learned method for motion deblurring. The learning is based on a conditional GAN and the content loss . DeblurGAN achieves state-of-the art performance both in the structural similarity measure and visual appearance. The quality of the deblurring model is also evaluated in a novel way on a real-world problem -- object detection on (de-)blurred images. The method is 5 times faster than the closest competitor -- DeepDeblur. We also introduce a novel method for generating synthetic motion blurred images from sharp ones, allowing realistic dataset augmentation. The model, code and the dataset are available at https://github.com/KupynOrest/DeblurGAN

MAGSAC: marginalizing sample consensus

A method called sigma-consensus is proposed to eliminate the need for a user-defined inlier-outlier threshold in RANSAC. Instead of estimating sigma, it is marginalized over a range of noise scales using a Bayesian estimator, i.e. the optimized model is obtained as the weighted average using the posterior probabilities as weights. Applying sigma-consensus, two methods are proposed: (i) a post-processing step which always improved the model quality on a wide range of vision problems without noticeable deterioration in processing time, i.e. at most 1-2 milliseconds; and (ii) a locally optimized RANSAC, called LO-MAGSAC, which includes sigma-consensus to the local optimization of LO-RANSAC. The method is superior to the state-of-the-art in terms of geometric accuracy on publicly available real world datasets for epipolar geometry (F and E), homography and affine transformation estimation.

E2E-MLT - an Unconstrained End-to-End Method for Multi-Language Scene Text

An end-to-end method for multi-language scene text localization, recognition and script identification is proposed. The approach is based on a set of convolutional neural nets. The method, called E2E-MLT, achieves state-of-the-art performance for both joint localization and script identification in natural images and in cropped word script identification. E2E-MLT is the first published multi-language OCR for scene text. The experiments show that obtaining accurate multi-language multi-script annotations is a challenging problem.

Working hard to know your neighbor's margins: Local descriptor learning loss

We introduce a novel loss for learning local feature descriptors which is inspired by the Lowe's matching criterion for SIFT. We show that the proposed loss that maximizes the distance between the closest positive and closest negative patch in the batch is better than complex regularization methods; it works well for both shallow and deep convolution network architectures. Applying the novel loss to the L2Net CNN architecture results in a compact descriptor -- it has the same dimensionality as SIFT (128) that shows state-of-art performance in wide baseline stereo, patch verification and instance retrieval benchmarks. It is fast, computing a descriptor takes about 1 millisecond on a low-end GPU.

Graph-Cut RANSAC

A novel method for robust estimation, called Graph-Cut RANSAC, GC-RANSAC in short, is introduced. To separate inliers and outliers, it runs the graph-cut algorithm in the local optimization (LO) step which is applied when a so-far-the-best model is found. The proposed LO step is conceptually simple, easy to implement, globally optimal and efficient. GC-RANSAC is shown experimentally, both on synthesized tests and real image pairs, to be more geometrically accurate than state-of-the-art methods on a range of problems, e.g. line fitting, homography, affine transformation, fundamental and essential matrix estimation. It runs in real-time for many problems at a speed approximately equal to that of the less accurate alternatives (in milliseconds on standard CPU).

Multi-Class Model Fitting by Energy Minimization and Mode-Seeking

We propose a general formulation, called Multi-X, for multi-class multi-instance model fitting - the problem of interpreting the input data as a mixture of noisy observations originating from multiple instances of multiple classes. We extend the commonly used alpha-expansion-based technique with a new move in the label space. The move replaces a set of labels with the corresponding density mode in the model parameter domain, thus achieving fast and robust optimization. Key optimization parameters like the bandwidth of the mode seeking are set automatically within the algorithm. Considering that a group of outliers may form spatially coherent structures in the data, we propose a cross-validation-based technique removing statistically insignificant instances. Multi-X outperforms significantly the state-of-the-art on publicly available datasets for diverse problems: multiple plane and rigid motion detection; motion segmentation; simultaneous plane and cylinder fitting; circle and line fitting.