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Finding Geometric Models by Clustering in the Consensus Space

Finding Geometric Models by Clustering in the Consensus Spac...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Daniel Barath Denys Rozumnyi Ivan Eichhardt Levente Hajder Jiri Matas Computer Vision and Geometry Group ETH Zurich Switzerland VRG Faculty of Electrical Engineering CTU in Prague Czech Republic TMEIC Corporation Americas Roanoke VA USA Eotvös Loránd University Budapest Hungary

We propose a new algorithm for finding an unknown number of geometric models, e.g., homographies. The problem is formalized as finding dominant model instances progressively without forming crisp point-to-model assignments. Dominant instances are found via a RANSAC-like sampling and a consolidation process driven by a model quality function considering previously proposed instances. New ones are found by clustering in the consensus space. This new formulation leads to a simple iterative algorithm with state-of-the-art accuracy while running in real-time on a number of vision problems - at least two orders of magnitude faster than the competitors on two-view motion estimation. Also, we propose a deterministic sampler reflecting the fact that real-world data tend to form spatially coherent structures. The sampler returns connected components in a progressively densified neighborhood-graph. We present a number of applications where the use of multiple geometric models improves accuracy. These include pose estimation from multiple generalized homographies; trajectory estimation of fast-moving objects; and we also propose a way of using multiple homographies in global SfM algorithms. Source code: https://***/danini/clustering-in-consensus-space.

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Shelf-Supervised Mesh Prediction in the Wild

Shelf-Supervised Mesh Prediction in the Wild

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Ye, Yufei Tulsiani, Shubham Gupta, Abhinav Carnegie Mellon Univ Pittsburgh PA 15213 USA Facebook AI Res Pittsburgh PA USA

ISBN: (纸本)9781665445092

We aim to infer 3D shape and pose of object from a single image and propose a learning-based approach that can train from unstructured image collections, supervised by only segmentation outputs from off-the-shelf recognition systems (i.e. `shelf-supervised'). We first infer a volumetric representation in a canonical frame, along with the camera pose. We enforce the representation geometrically consistent with both appearance and masks, and also that the synthesized novel views are indistinguishable from image collections. The coarse volumetric prediction is then converted to a mesh-based representation, which is further refined in the predicted camera frame. These two steps allow both shape-pose factorization from image collections and per-instance reconstruction in finer details. We examine the method on both synthetic and the real-world datasets and demonstrate its scalability on 50 categories in the wild, an order of magnitude more classes than existing works.

关键词： Visualization Three-dimensional displays Shape Scalability Lighting Predictive models Rendering (computer graphics)

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End-to-End Human Pose and Mesh Reconstruction with Transformers

End-to-End Human Pose and Mesh Reconstruction with Transform...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Lin, Kevin Wang, Lijuan Liu, Zicheng Microsoft Albuquerque NM 87108 USA

ISBN: (纸本)9781665445092

We present a new method, called MEsh TRansfOrmer (METRO), to reconstruct 3D human pose and mesh vertices from a single image. Our method uses a transformer encoder to jointly model vertex-vertex and vertex-joint interactions, and outputs 3D joint coordinates and mesh vertices simultaneously. Compared to existing techniques that regress pose and shape parameters, METRO does not rely on any parametric mesh models like SMPL, thus it can be easily extended to other objects such as hands. We further relax the mesh topology and allow the transformer self-attention mechanism to freely attend between any two vertices, making it possible to learn non-local relationships among mesh vertices and joints. With the proposed masked vertex modeling, our method is more robust and effective in handling challenging situations like partial occlusions. METRO generates new state-of-the-art results for human mesh reconstruction on the public Human3.6M and 3DPW datasets. Moreover, we demonstrate the generalizability of METRO to 3D hand reconstruction in the wild, outperforming existing state-of-the-art methods on FreiHAND dataset.

关键词： Solid modeling computer vision Three-dimensional displays Shape Transformers Topology pattern recognition

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Deep Gradient Projection Networks for Pan-sharpening

Deep Gradient Projection Networks for Pan-sharpening

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Xu, Shuang Zhang, Jiangshe Zhao, Zixiang Sun, Kai Liu, Junmin Zhang, Chunxia Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Peoples R China

ISBN: (纸本)9781665445092

Pan-sharpening is an important technique for remote sensing imaging systems to obtain high resolution multispectral images. Recently, deep learning has become the most popular tool for pan-sharpening. This paper develops a model-based deep pan-sharpening approach. Specifically, two optimization problems regularized by the deep prior are formulated, and they are separately responsible for the generative models for panchromatic images and low resolution multispectral images. Then, the two problems are solved by a gradient projection algorithm, and the iterative steps are generalized into two network blocks. By alternatively stacking the two blocks, a novel network, called gradient projection based pan-sharpening neural network, is constructed. The experimental results on different kinds of satellite datasets demonstrate that the new network outperforms state-of-the-art methods both visually and quantitatively.

关键词： Image resolution Satellites Stacking Neural networks Tools Iterative algorithms pattern recognition

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Can audio-visual integration strengthen robustness under multimodal attacks?

Can audio-visual integration strengthen robustness under mul...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Tian, Yapeng Xu, Chenliang Univ Rochester Rochester NY 14627 USA

ISBN: (纸本)9781665445092

In this paper, we propose to make a systematic study on machines' multisensory perception under attacks. We use the audio-visual event recognition task against multimodal adversarial attacks as a proxy to investigate the robustness of audio-visual learning. We attack audio, visual, and both modalities to explore whether audio-visual integration still strengthens perception and how different fusion mechanisms affect the robustness of audio-visual models. For interpreting the multimodal interactions under attacks, we learn a weakly-supervised sound source visual localization model to localize sounding regions in videos. To mitigate multimodal attacks, we propose an audio-visual defense approach based on an audio-visual dissimilarity constraint and external feature memory banks. Extensive experiments demonstrate that audio-visual models are susceptible to multimodal adversarial attacks;audio-visual integration could decrease the model robustness rather than strengthen under multimodal attacks;even a weaklysupervised sound source visual localization model can be successfully fooled;our defense method can improve the invulnerability of audio-visual networks without significantly sacrificing clean model performance. The source code and pre-trained models are released in https://***/YapengTian/AV-Robustness-cvpr21.

关键词： Location awareness Visualization computer vision Systematics Codes Computational modeling Robustness

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GazeCaps: Gaze Estimation with Self-Attention-Routed Capsules

GazeCaps: Gaze Estimation with Self-Attention-Routed Capsule...

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2023 ieee/cvf conference on computer vision and pattern recognition Workshops, cvprW 2023

作者： Wang, Hengfei Oh, Jun O Jin Chang, Hyung Na, Jin Hee Tae, Minwoo Zhang, Zhongqun Choi, Sang-Il University of Birmingham United Kingdom Dankook University Korea Republic of VTouch Inc

ISBN: (纸本)9798350302493

Gaze estimation is the task of estimating eye gaze from facial features. People tend to infer gaze by considering different facial properties from the whole image and their relations. However, existing methods rarely consider these various properties. In this paper, we propose a novel GazeCaps framework that represents various facial properties as different capsules. The capsules respond sensitively to transforms of facial properties by vectorial expression, which is effective for gaze estimation in which many facial components are nonlinearly transformed according to the direction of the head in addition to the perspective. Furthermore, we propose a Self-Attention Routing (SAR) module which can dynamically allocate attention to different capsules that contain important information and can be optimized as a single process without iterations. Through rigorous experiments, we confirm that the proposed method achieves state-of-the-art performance on various benchmarks. We also detail the generalization performance of the proposed model through a cross-dataset evaluation. © 2023 ieee.

关键词： computer vision

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Stable View Synthesis

Stable View Synthesis

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Riegler, Gernot Koltun, Vladlen Intel Labs Hillsboro OR 97124 USA

ISBN: (纸本)9781665445092

We present Stable View Synthesis (SVS). Given a set of source images depicting a scene from freely distributed viewpoints, SVS synthesizes new views of the scene. The method operates on a geometric scaffold computed via structure-from-motion and multi-view stereo. Each point on this 3D scaffold is associated with view rays and corresponding feature vectors that encode the appearance of this point in the input images. The core of SVS is view-dependent on-surface feature aggregation, in which directional feature vectors at each 3D point are processed to produce a new feature vector for a ray that maps this point into the new target view. The target view is then rendered by a convolutional network from a tensor of features synthesized in this way for all pixels. The method is composed of differentiable modules and is trained end-to-end. It supports spatially-varying view-dependent importance weighting and feature transformation of source images at each point;spatial and temporal stability due to the smooth dependence of on-surface feature aggregation on the target view;and synthesis of view-dependent effects such as specular reflection. Experimental results demonstrate that SVS outperforms state-of-the-art view synthesis methods both quantitatively and qualitatively on three diverse real-world datasets, achieving unprecedented levels of realism in free-viewpoint video of challenging large-scale scenes.

关键词： Convolutional codes computer vision Three-dimensional displays Tensors Reflection pattern recognition

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Video Event Restoration Based on Keyframes for Video Anomaly Detection

Video Event Restoration Based on Keyframes for Video Anomaly...

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conference on computer vision and pattern recognition (cvpr)

作者： Zhiwei Yang Jing Liu Zhaoyang Wu Peng Wu Xiaotao Liu Guangzhou Institute of Technology Xidian University Guangzhou China School of Computer Science Northwestern Polytechnical University Xi'an China

Video anomaly detection (VAD) is a significant computer vision problem. Existing deep neural network (DNN) based VAD methods mostly follow the route of frame reconstruction or frame prediction. However, the lack of mining and learning of higher-level visual features and temporal context relationships in videos limits the further performance of these two approaches. Inspired by video codec theory, we introduce a brand-new VAD paradigm to break through these limitations: First, we propose a new task of video event restoration based on keyframes. Encouraging DNN to infer missing multiple frames based on video keyframes so as to restore a video event, which can more effectively motivate DNN to mine and learn potential higher-level visual features and comprehensive temporal context relationships in the video. To this end, we propose a novel U-shaped Swin Transformer Network with Dual Skip Connections (USTN-DSC) for video event restoration, where a cross-attention and a temporal upsampling residual skip connection are introduced to further assist in restoring complex static and dynamic motion object features in the video. In addition, we propose a simple and effective adjacent frame difference loss to constrain the motion consistency of the video sequence. Extensive experiments on benchmarks demonstrate that USTN-DSC outperforms most existing methods, validating the effectiveness of our method.

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Convolutional Hough Matching Networks

Convolutional Hough Matching Networks

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Min, Juhong Cho, Minsu POSTECH CSE Pohang South Korea POSTECH GSAI Pohang South Korea

ISBN: (纸本)9781665445092

Despite advances in feature representation, leveraging geometric relations is crucial for establishing reliable visual correspondences under large variations of images. In this work we introduce a Hough transform perspective on convolutional matching and propose an effective geometric matching algorithm, dubbed Convolutional Hough Matching (CHM). The method distributes similarities of candidate matches over a geometric transformation space and evaluate them in a convolutional manner. We cast it into a trainable neural layer with a semi-isotropic high-dimensional kernel, which learns non-rigid matching with a small number of interpretable parameters. To validate the effect, we develop the neural network with CHM layers that perform convolutional matching in the space of translation and scaling. Our method sets a new state of the art on standard benchmarks for semantic visual correspondence, proving its strong robustness to challenging intra-class variations.

关键词： Visualization computer vision Semantics Neural networks Transforms Benchmark testing Robustness

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Privacy Preserving Localization and Mapping from Uncalibrated Cameras

Privacy Preserving Localization and Mapping from Uncalibrate...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Geppert, Marcel Larsson, Viktor Speciale, Pablo Schonberger, Johannes L. Pollefeys, Marc Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland Microsoft Redmond WA USA

ISBN: (纸本)9781665445092

Recent works on localization and mapping from privacy preserving line features have made significant progress towards addressing the privacy concerns arising from cloud-based solutions in mixed reality and robotics. The requirement for calibrated cameras is a fundamental limitation for these approaches, which prevents their application in many crowd-sourced mapping scenarios. In this paper, we propose a solution to the uncalibrated privacy preserving localization and mapping problem. Our approach simultaneously recovers the intrinsic and extrinsic calibration of a camera from line-features only. This enables uncalibrated devices to both localize themselves within an existing map as well as contribute to the map, while preserving the privacy of the image contents. Furthermore, we also derive a solution to bootstrapping maps from scratch using only uncalibrated devices. Our approach provides comparable performance to the calibrated scenario and the privacy compromising alternatives based on traditional point features.

关键词： Location awareness Performance evaluation Privacy Robot vision systems Pose estimation Poles and towers Cameras

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