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Context-aware Synthesis for Video Frame Interpolation 31

Context-aware Synthesis for Video Frame Interpolation

引用

31st ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Niklaus, Simon Liu, Feng Portland State Univ Portland OR 97207 USA

ISBN: (纸本)9781538664209

Video frame interpolation algorithms typically estimate optical flow or its variations and then use it to guide the synthesis of an intermediate frame between two consecutive original frames. To handle challenges like occlusion, bidirectional flow between the two input frames is often estimated and used to warp and blend the input frames. However, how to effectively blend the two warped frames still remains a challenging problem. This paper presents a context-aware synthesis approach that warps not only the input frames but also their pixel-wise contextual information and uses them to interpolate a high-quality intermediate frame. Specifically, we first use a pre-trained neural network to extract per-pixel contextual information for input frames. We then employ a state-of-the-art optical flow algorithm to estimate bidirectional flow between them and pre-warp both input frames and their context maps. Finally, unlike common approaches that blend the pre-warped frames, our method feeds them and their context maps to a video frame synthesis neural network to produce the interpolated frame in a context-aware fashion. Our neural network is fully convolutional and is trained end to end. Our experiments show that our method can handle challenging scenarios such as occlusion and large motion and outperforms representative state-of-the-art approaches.

关键词： Interpolation Adaptive optics Neural networks Optical computing Optical imaging computer vision Estimation

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Rolling Shutter and Radial Distortion are Features for High Frame Rate Multi-camera Tracking 31

Rolling Shutter and Radial Distortion are Features for High ...

引用

31st ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Bapat, Akash Price, True Frahm, Jan-Michael Univ N Carolina Dept Comp Sci Chapel Hill NC 27515 USA

ISBN: (纸本)9781538664209

Traditionally, camera-based tracking approaches have treated rolling shutter and radial distortion as imaging artifacts that have to be overcome and corrected for in order to apply standard camera models and scene reconstruction methods. In this paper, we introduce a novel multi-camera tracking approach that for the first time jointly leverages the information introduced by rolling shutter and radial distortion as a feature to achieve superior performance with respect to high frequency camera pose estimation. In particular, our system is capable of attaining high tracking rates that were previously unachievable. Our approach explicitly leverages rolling shutter capture and radial distortion to process individual rows, rather than entire image frames, for accurate camera motion estimation. We estimate a per row 6 DoF pose of a rolling shutter camera by tracking multiple points on a radially distorted row whose rays span a curved surface in 3D space. Although tracking systems for rolling shutter cameras exist, we are the first to leverage radial distortion to measure a per-row pose enabling us to use less than half the number of cameras required by the previous state of the art. We validate our system on both synthetic and real imagery.

关键词： Cameras Distortion Tracking Three-dimensional displays computer vision Lenses

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Iso-Points: Optimizing Neural Implicit Surfaces with Hybrid Representations

Iso-Points: Optimizing Neural Implicit Surfaces with Hybrid ...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Wang Yifan Wu, Shihao Oztireli, Cengiz Sorkine-Hornung, Olga Swiss Fed Inst Technol Zurich Switzerland Univ Cambridge Cambridge England

ISBN: (数字)9781665445092

ISBN: (纸本)9781665445092

Neural implicit functions have emerged as a powerful representation for surfaces in 3D. Such a function can encode a high quality surface with intricate details into the parameters of a deep neural network. However, optimizing for the parameters for accurate and robust reconstructions remains a challenge, especially when the input data is noisy or incomplete. In this work, we develop a hybrid neural surface representation that allows us to impose geometry-aware sampling and regularization, which significantly improves the fidelity of reconstructions. We propose to use iso-points as an explicit representation for a neural implicit function. These points are computed and updated on-the-fly during training to capture important geometric features and impose geometric constraints on the optimization. We demonstrate that our method can be adopted to improve state-of-the-art techniques for reconstructing neural implicit surfaces from multi-view images or point clouds. Quantitative and qualitative evaluations show that, compared with existing sampling and optimization methods, our approach allows faster convergence, better generalization, and accurate recovery of details and topology.

关键词： Training Deep learning Surface reconstruction Three-dimensional displays Optimization methods Topology pattern recognition

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Cross-Domain Correlation Distillation for Unsupervised Domain Adaptation in Nighttime Semantic Segmentation

Cross-Domain Correlation Distillation for Unsupervised Domai...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Gao, Huan Guo, Jichang Wang, Guoli Zhang, Qian Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China Horizon Robot Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The performance of nighttime semantic segmentation is restricted by the poor illumination and a lack of pixelwise annotation, which severely limit its application in autonomous driving. Existing works, e.g., using the twilight as the intermediate target domain to perform the adaptation from daytime to nighttime, may fail to cope with the inherent difference between datasets caused by the camera equipment and the urban style. Faced with these two types of domain shifts, i.e., the illumination and the inherent difference of the datasets, we propose a novel domain adaptation framework via cross-domain correlation distillation, called CCDistill. The invariance of illumination or inherent difference between two images is fully explored so as to make up for the lack of labels for nighttime images. Specifically, we extract the content and style knowledge contained in features, calculate the degree of inherent or illumination difference between two images. The domain adaptation is achieved using the invariance of the same kind of difference. Extensive experiments on Dark Zurich and ACDC demonstrate that CCDistill achieves the state-of-the-art performance for nighttime semantic segmentation. Notably, our method is a one-stage domain adaptation network which can avoid affecting the inference time.

关键词： Image segmentation computer vision Correlation Shape Semantics Lighting Feature extraction

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Single Image Reflection Removal with Absorption Effect

Single Image Reflection Removal with Absorption Effect

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zheng, Qian Shi, Boxin Chen, Jinnan Jiang, Xudong Duan, Ling-Yu Kot, Alex C. Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore Peking Univ Dept Comp Sci & Technol NELVT Beijing Peoples R China Peking Univ Inst Artificial Intelligence Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9781665445092

In this paper, we consider the absorption effect for the problem of single image reflection removal. We show that the absorption effect can be numerically approximated by the average of refractive amplitude coefficient map. We then reformulate the image formation model and propose a two-step solution that explicitly takes the absorption effect into account. The first step estimates the absorption effect from a reflection-contaminated image, while the second step recovers the transmission image by taking a reflection-contaminated image and the estimated absorption effect as the input. Experimental results on four public datasets show that our two-step solution not only successfully removes reflection artifact, but also faithfully restores the intensity distortion caused by the absorption effect. Our ablation studies further demonstrate that our method achieves superior performance on the recovery of overall intensity and has good model generalization capacity. The code is available at https://***/q-zh/absorption.

关键词： computer vision Absorption Lighting Estimation Distortion Reflection Data models

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Structure from Small Baseline Motion with Central Panoramic Cameras

Structure from Small Baseline Motion with Central Panoramic ...

引用

conference on computer vision and pattern recognition Workshop, CVPRW 2003

作者： Shakernia, Omid Vidal, René Sastry, Shankar Department of Electrical Engineering and Computer Sciences UC Berkeley United States

ISBN: (纸本)0769519008

In applications of egomotion estimation, such as real-time vision-based navigation, one must deal with the double-edged sword of small relative motions between images. On one hand, tracking feature points is easier, while on the other, two-view structure-from-motion algorithms are poorly conditioned due to the low signal-to-noise ratio. In this paper, we derive a multi-frame structure from motion algorithm for calibrated central panoramic cameras. Our algorithm avoids the conditioning problem by explicitly incorporating the small baseline assumption in the algorithm's design. The proposed algorithm is linear, amenable to real-time implementation, and performs well in the small baseline domain for which it is designed. © 2003 ieee.

关键词： Real time control

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Instance Shadow Detection

Instance Shadow Detection

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Wang, Tianyu Hu, Xiaowei Wang, Qiong Heng, Pheng-Ann Fu, Chi-Wing Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China Chinese Acad Sci Shenzhen Key Lab Virtual Real & Human Interact Te Shenzhen Inst Adv Technol Beijing Peoples R China

ISBN: (纸本)9781728171685

Instance shadow detection is a brand new problem, aiming to find shadow instances paired with object instances. To approach it, we first prepare a new dataset called SOBA, named after Shadow-OBject Association, with 3,623 pairs of shadow and object instances in 1,000 photos, each with individually-labeled masks. Second, we design LISA, named after Light-guided Instance Shadow-object Association, an end-to-end framework to automatically predict the shadow and object instances, together with the shadow-object associations and light direction. Then, we pair up the predicted shadow and object instances and match them with the predicted shadow-object associations to generate the final results. In our evaluations, we formulate a new metric named the shadow-object average precision to measure the performance of our results. Further, we conducted various experiments and demonstrate our method's applicability to light direction estimation and photo editing.

关键词： computer vision

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MODEL-BASED INVARIANTS FOR 3D vision

MODEL-BASED INVARIANTS FOR 3D VISION

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1993 ieee computer SOC CONF ON computer vision AND pattern recognition ( CVPR 93 )

作者： WEINSHALL, D Hebrew Univ of Jerusalem Jerusalem Israel

ISBN: (纸本)081863880X

We describe a hierarchical representation of 3D shape of objects, which is invariant to affine and rigid 3D transformations. Model-based invariant functions of general 3D objects are defined and constructed using this representation. A linear algorithm for invariant structure from motion from a sequence of images is outlined. The model-based invariant functions are used as model-based object recognition operators, operating on raw image features. An efficient invariant implementation of alignment and geometric hashing is also discussed. The complete description of this work is given in literature.

关键词： computer vision

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Language-Guided Audio-Visual Source Separation via Trimodal Consistency

Language-Guided Audio-Visual Source Separation via Trimodal ...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Tan, Reuben Ray, Arijit Burns, Andrea Plummer, Bryan A. Salamon, Justin Nieto, Oriol Russell, Bryan Saenko, Kate Boston Univ Boston MA 02215 USA Adobe Res San Francisco CA USA IBM Res MIT IBM Watson AI Lab Cambridge MA USA

ISBN: (纸本)9798350301298

We propose a self-supervised approach for learning to perform audio source separation in videos based on natural language queries, using only unlabeled video and audio pairs as training data. A key challenge in this task is learning to associate the linguistic description of a sound-emitting object to its visual features and the corresponding components of the audio waveform, all without access to annotations during training. To overcome this challenge, we adapt off-the-shelf vision-language foundation models to provide pseudo-target supervision via two novel loss functions and encourage a stronger alignment between the audio, visual and natural language modalities. During inference, our approach can separate sounds given text, video and audio input, or given text and audio input alone. We demonstrate the effectiveness of our self-supervised approach on three audio-visual separation datasets, including MUSIC, SOLOS and AudioSet, where we outperform state-of-the-art strongly supervised approaches despite not using object detectors or text labels during training. Our project page including publicly available code can be found at https://***/rxtan/projects/VAST.

关键词： and reasoning language vision

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Learning to Detect Scene Landmarks for Camera Localization

Learning to Detect Scene Landmarks for Camera Localization

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Do, Tien Miksik, Ondrej DeGol, Joseph Park, Hyun Soo Sinha, Sudipta N. Univ Minnesota Minneapolis MN 55455 USA Microsoft Redmond WA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Modern camera localization methods that use image retrieval, feature matching, and 3D structure-based pose estimation require long-term storage of numerous scene images or a vast amount of image features. This can make them unsuitable for resource constrained VR/AR devices and also raises serious privacy concerns. We present a new learned camera localization technique that eliminates the need to store features or a detailed 3D point cloud. Our key idea is to implicitly encode the appearance of a sparse yet salient set of 3D scene points into a convolutional neural network (CNN) that can detect these scene points in query images whenever they are visible. We refer to these points as scene landmarks. We also show that a CNN can be trained to regress bearing vectors for such landmarks even when they are not within the camera's field-of-view. We demonstrate that the predicted landmarks yield accurate pose estimates and that our method outperforms DSAC*, the state-of-the-art in learned localization. Furthermore, extending HLoc (an accurate method) by combining its correspondences with our predictions boosts its accuracy even further.

关键词： Location awareness Point cloud compression Privacy computer vision Three-dimensional displays Pose estimation Image retrieval

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