Assembling convolution neural networks for automatic viewing transformation

Images taken under different camera poses are rotated or distorted, which leads to poor perception experiences. This paper proposes a new framework to automatically transform the images to the conformable view setting by assembling different convolution neural networks. Specifically, a referential 3D ground plane is firstly derived from the RGB image and a novel projection mapping algorithm is developed to achieve automatic viewing transformation. Extensive experimental results demonstrate that the proposed method outperforms the state-ofthe-art vanishing points based methods by a large margin in terms of accuracy and robustness

Video Upright Adjustment and Stabilization

Upright adjustment, Video stabilization, Camera pathWe propose a novel video upright adjustment method that can reliably correct slanted video contents that are often found in casual videos. Our approach combines deep learning and Bayesian inference to estimate accurate rotation angles from video frames. We train a convolutional neural network to obtain initial estimates of the rotation angles of input video frames. The initial estimates from the network are temporally inconsistent and inaccurate. To resolve this, we use Bayesian inference. We analyze estimation errors of the network, and derive an error model. We then use the error model to formulate video upright adjustment as a maximum a posteriori problem where we estimate consistent rotation angles from the initial estimates, while respecting relative rotations between consecutive frames. Finally, we propose a joint approach to video stabilization and upright adjustment, which minimizes information loss caused by separately handling stabilization and upright adjustment. Experimental results show that our video upright adjustment method can effectively correct slanted video contents, and its combination with video stabilization can achieve visually pleasing results from shaky and slanted videos.openI. INTRODUCTION 1.1. Related work II. ROTATION ESTIMATION NETWORK III. ERROR ANALYSIS IV. VIDEO UPRIGHT ADJUSTMENT 4.1. Initial angle estimation 4.2. Robust angle estimation 4.3. Optimization 4.4. Warping V. JOINT UPRIGHT ADJUSTMENT AND STABILIZATION 5.1. Bundled camera paths for video stabilization 5.2. Joint approach VI. EXPERIMENTS VII. CONCLUSION ReferencesCNN)을 훈련시킨다. 신경망의 초기 추정치는 완전히 정확하지 않으며 시간적으로도 일관되지 않는다. 이를 해결하기 위해 베이지안 인퍼런스를 사용한다. 본 논문은 신경망의 추정 오류를 분석하고 오류 모델을 도출한다. 그런 다음 오류 모델을 사용하여 연속 프레임 간의 상대 회전 각도(Relative rotation angle)를 반영하면서 초기 추정치로부터 시간적으로 일관된 회전 각도를 추정하는 최대 사후 문제(Maximum a posteriori problem)로 동영상 수평 보정을 공식화한다. 마지막으로, 동영상 수평 보정 및 동영상 안정화(Video stabilization)에 대한 동시 접근 방법을 제안하여 수평 보정과 안정화를 별도로 수행할 때 발생하는 공간 정보 손실과 연산량을 최소화하며 안정화의 성능을 최대화한다. 실험 결과에 따르면 동영상 수평 보정으로 기울어진 동영상을 효과적으로 보정할 수 있으며 동영상 안정화 방법과 결합하여 흔들리고 기울어진 동영상으로부터 시각적으로 만족스러운 새로운 동영상을 획득할 수 있다.본 논문은 일반인들이 촬영한 동영상에서 흔히 발생하는 문제인 기울어짐을 제거하여 수평이 올바른 동영상을 획득할 수 있게 하는 동영상 수평 보정(Video upright adjustment) 방법을 제안한다. 본 논문의 접근 방식은 딥 러닝(Deep learning)과 베이지안 인퍼런스(Bayesian inference)를 결합하여 동영상 프레임(Frame)에서 정확한 각도를 추정한다. 먼저 입력 동영상 프레임의 회전 각도의 초기 추정치를 얻기 위해 회선 신경망(Convolutional neural networkMasterdCollectio

Beyond Geo-localization: Fine-grained Orientation of Street-view Images by Cross-view Matching with Satellite Imagery

Street-view imagery provides us with novel experiences to explore different places remotely. Carefully calibrated street-view images (e.g. Google Street View) can be used for different downstream tasks, e.g. navigation, map features extraction. As personal high-quality cameras have become much more affordable and portable, an enormous amount of crowdsourced street-view images are uploaded to the internet, but commonly with missing or noisy sensor information. To prepare this hidden treasure for "ready-to-use" status, determining missing location information and camera orientation angles are two equally important tasks. Recent methods have achieved high performance on geo-localization of street-view images by cross-view matching with a pool of geo-referenced satellite imagery. However, most of the existing works focus more on geo-localization than estimating the image orientation. In this work, we re-state the importance of finding fine-grained orientation for street-view images, formally define the problem and provide a set of evaluation metrics to assess the quality of the orientation estimation. We propose two methods to improve the granularity of the orientation estimation, achieving 82.4% and 72.3% accuracy for images with estimated angle errors below 2 degrees for CVUSA and CVACT datasets, corresponding to 34.9% and 28.2% absolute improvement compared to previous works. Integrating fine-grained orientation estimation in training also improves the performance on geo-localization, giving top 1 recall 95.5%/85.5% and 86.8%/80.4% for orientation known/unknown tests on the two datasets.Comment: This paper has been accepted by ACM Multimedia 2022. The version contains additional supplementary material

Robust upright adjustment of 360 spherical panoramas

With the recent advent of 360 cameras, spherical panorama images are becoming more popular and widely available. In a spherical panorama, alignment of the scene orientation to the image axes is important for providing comfortable and pleasant viewing experiences using VR headsets and traditional displays. This paper presents an automatic method for upright adjustment of 360 spherical panorama images without any prior information, such as depths and gyro sensor data. We take the Atlanta world assumption and use the horizontal and vertical lines in the scene to formulate a cost function for upright adjustment. In addition to fast optimization of the cost function, our method includes outlier handling to improve the robustness and accuracy of upright adjustment. Our method produces visually pleasing results for a variety of real-world spherical panoramas in less than a second, and the accuracy is verified using ground-truth data. ? 2017, Springer-Verlag Berlin Heidelberg.112sciescopu