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Pose Estimation for Two-View Panoramas based on Keypoint Matching: a Comparative Study and Critical Analysis

Pose Estimation for Two-View Panoramas based on Keypoint Mat...

引用

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

作者： Murrugarra-Llerena, Jeffri da Silveira, Thiago L. T. Jung, Claudio R. Univ Fed Rio Grande do Sul Inst Informat Porto Alegre RS Brazil

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Pose estimation is a crucial problem in several computer vision and robotics applications. For the two-view scenario, the typical pipeline consists of finding point correspondences between the two views and using them to estimate the pose. However, most available keypoint extraction and matching methods were designed to work with perspective images and may fail under not-affine distortions present in wide-angle or omnidirectional media, which are becoming increasingly popular in recent years. This paper presents a comprehensive comparative analysis of different keypoint matching algorithms for panoramas coupled to different linear and non-linear approaches for pose estimation. As an additional contribution, we explore a recent approach for mitigating spherical distortions using tangent plane projections, which can be coupled with any planar descriptor, and allows the adaptation of recent learning-based methods. We evaluate the combination of keypoint matching and pose estimation methods using the rotation and translation error of the estimated pose in different scenarios (indoor and outdoor), and our results indicate that SPHORB and "tangent SIFT" are competitive algorithms. We also show that tangent plane adaptations frequently present competitive results, and some optimization steps consistently improve the performance in all methods. We provide code at https://***/Artcs1/Keypoints

关键词： Learning systems Couplings computer vision Planarization Pose estimation Nonlinear distortion Pipelines

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Uniform Priors for Data-Efficient Learning

Uniform Priors for Data-Efficient Learning

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

作者： Sinha, Samarth Roth, Karsten Goyal, Anirudh Ghassemi, Marzyeh Akata, Zeynep Larochelle, Hugo Garg, Animesh Univ Toronto Toronto ON Canada Univ Tubingen Tubingen Germany MILA Montreal PQ Canada MIT Cambridge MA 02139 USA MPI IS Stuttgart Germany Google Mountain View CA 94043 USA Vector Inst Bengaluru India Nvidia Santa Clara CA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Few or zero-shot adaptation to novel tasks is important for the scalability and deployment of machine learning models. It is therefore crucial to find properties that encourage more transferable features in deep networks for generalization. In this paper, we show that models that learn uniformly distributed features from the training data, are able to perform better transfer learning at test-time. Motivated by this, we evaluate our method: uniformity regularization (UR) on its ability to facilitate adaptation to unseen tasks and data on six distinct domains: Few-Learning with Images, Few-shot Learning with Language, Deep Metric Learning, 0-Shot Domain Adaptation, Out-of-Distribution classification, and Neural Radiance Fields. Across all experiments, we show that using UR, we are able to learn robust vision systems which consistently offer benefits over baselines trained without uniformity regularization and are able to achieve state-of-the-art performance in Deep Metric Learning, Few-shot learning with images and language.

关键词： Measurement Training Deep learning Adaptation models Scalability Machine vision Transfer learning

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Consistency-based Active Learning for Object Detection

Consistency-based Active Learning for Object Detection

引用

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

作者： Yu, Weiping Zhu, Sijie Yang, Taojiannan Chen, Chen Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Univ Cent Florida Ctr Res Comp Vis Orlando FL 32816 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Active learning aims to improve the performance of the task model by selecting the most informative samples with a limited budget. Unlike most recent works that focus on applying active learning for image classification, we propose an effective Consistency-based Active Learning method for object Detection (CALD), which fully explores the consistency between the original and augmented data. CALD has three appealing benefits. (i) CALD is systematically designed by investigating the weaknesses of existing active learning methods, which do not take the unique challenges of object detection into account. (ii) CALD unifies box regression and classification with a single metric, which is not concerned with active learning methods for classification. CALD also focuses on the most informative local region rather than the whole image, which is beneficial for object detection. (iii) CALD not only gauges individual information for sample selection but also leverages mutual information to encourage a balanced data distribution. Extensive experiments show that CALD significantly outperforms existing state-of-the-art task-agnostic and detection-specific active learning methods on general object detection datasets. Based on the Faster R-CNN detector, CALD consistently surpasses the baseline method (random selection) by 2.9/2.8/0.8 mAP on average on PASCAL VOC 2007, PASCAL VOC 2012, and MS COCO.

关键词： Learning systems Measurement computer vision conferences Object detection Detectors pattern recognition

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Towards Real-world Shadow Removal with a Shadow Simulation Method and a Two-stage Framework

Towards Real-world Shadow Removal with a Shadow Simulation M...

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

作者： Gao, Jianhao Zheng, Quanlong Guo, Yandong Wuhan Univ Wuhan Peoples R China OPPO Res Inst Shenzhen Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Shadow removal is an important yet challenging restoration task. State-of-the-art shadow removal methods usually require paired datasets for training. Existing shadow removal datasets lack large-scale quantity and scene diversity. Hence, models trained on such datasets have poor generalization ability. This paper proposes a simple yet robust shadow simulation method to simulate shadow on the grayscale. The proposed shadow simulation method can be applied to arbitrary shadow-free images and masks to generate corresponding shadow images. With our shadow simulation method, we can generate a large-scale and diverse paired shadow removal dataset. Besides, we introduce a two-stage framework, Gray-to-Color Shadow Removal Network (G2C-DeshadowNet), for shadow removal. The first stage is a Grayscale Enhancement Network, which attempts to remove shadows on the grayscale. The second stage is a Colorization Network, which attempts to colorize the grayscale shadow-free image. Extensive experiments on ISTD+, SRD, and SBU datasets show that G2C-DeshadowNet outperforms state-of-the-art methods and has better generalization ability. We will release our code at https://***/jianhaogao/Shadow-Removal-with-Two-stage-Framework.

关键词： Training computer vision Image color analysis conferences Computational modeling Gray-scale Robustness

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SoccerNet-Tracking: Multiple Object Tracking Dataset and Benchmark in Soccer Videos

SoccerNet-Tracking: Multiple Object Tracking Dataset and Ben...

引用

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

作者： Cioppa, Anthony Giancola, Silvio Deliege, Adrien Kang, Le Zhou, Xin Cheng, Zhiyu Ghanem, Bernard Van Droogenbroeck, Marc Univ Liege Liege Belgium KAUST Thuwal Saudi Arabia Baidu Res Beijing Peoples R China

ISBN: (纸本)9781665487399

Tracking objects in soccer videos is extremely important to gather both player and team statistics, whether it is to estimate the total distance run, the ball possession or the team formation. Video processing can help automating the extraction of those information, without the need of any invasive sensor, hence applicable to any team on any stadium. Yet, the availability of datasets to train learnable models and benchmarks to evaluate methods on a common testbed is very limited. In this work, we propose a novel dataset for multiple object tracking composed of 200 sequences of 30s each, representative of challenging soccer scenarios, and a complete 45-minutes half-time for long-term tracking. The dataset is fully annotated with bounding boxes and tracklet IDs, enabling the training of MOT baselines in the soccer domain and a full benchmarking of those methods on our segregated challenge sets. Our analysis shows that multiple player, referee and ball tracking in soccer videos is far from being solved, with several improvement required in case of fast motion or in scenarios of severe occlusion.

关键词： Training computer vision conferences Benchmark testing Object tracking Data mining Sports

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PanoPoint: Self-Supervised Feature Points Detection and Description for 360° Panorama

PanoPoint: Self-Supervised Feature Points Detection and Desc...

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2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Zhang, Hengzhi Yi, Hong Jia, Haijing Wang, Wei Odamaki, Makoto Co. Ltd. Beijing China Ricoh Company Ltd. Japan

ISBN: (纸本)9798350302493

We introduce PanoPoint, the joint feature point detection and description applied to the nonlinear distortions and the multi-view geometry problems between 360° panoramas. Our fully convolutional model operates directly in panoramas and computes pixel-level feature point locations and associated descriptors in a single forward pass rather than performing image preprocessing (e.g. panorama to Cubemap) followed by feature detection and description. To train the PanoPoint model, we propose PanoMotion, which simulates the representation between different viewpoints and generates warped panoramas. Moreover, we propose PanoMotion Adaptation, a multi-viewpoint adaptation annotation approach for boosting feature point detection repeatability instead of manual labelling. We train on the annotated synthetic dataset generated by the above method, which outperforms the traditional and other learned approaches and achieves state-of-the-art results on repeatability, localization accuracy, point correspondence precision and real-time metrics, especially for panoramas with significant viewpoint and illumination changes. © 2023 ieee.

关键词： computer vision

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Octree Transformer: Autoregressive 3D Shape Generation on Hierarchically Structured Sequences

Octree Transformer: Autoregressive 3D Shape Generation on Hi...

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Ibing, Moritz Kobsik, Gregor Kobbelt, Leif Rwth Aachen University Visual Computing Institute Germany

ISBN: (纸本)9798350302493

Autoregressive models have proven to be very powerful in NLP text generation tasks and lately have gained popularity for image generation as well. However, they have seen limited use for the synthesis of 3D shapes so far. This is mainly due to the lack of a straightforward way to linearize 3D data as well as to scaling problems with the length of the resulting sequences when describing complex shapes. In this work we address both of these problems. We use octrees as a compact hierarchical shape representation that can be sequentialized by traversal ordering. Moreover, we introduce an adaptive compression scheme, that significantly reduces sequence lengths and thus enables their effective generation with a transformer, while still allowing fully autoregressive sampling and parallel training. We demonstrate the performance of our model by performing superresolution and comparing against the state-of-the-art in shape generation. © 2023 ieee.

关键词： computer vision

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Embedding Arithmetic of Multimodal Queries for Image Retrieval

Embedding Arithmetic of Multimodal Queries for Image Retriev...

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

作者： Couairon, Guillaume Douze, Matthijs Cord, Matthieu Schwenk, Holger Meta AI Menlo Pk CA 94025 USA Sorbonne Univ Paris France Valeo St Ouen Sur Seine France

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Latent text representations exhibit geometric regularities, such as the famous analogy: queen is to king what woman is to man. Such structured semantic relations were not demonstrated on image representations. Recent works aiming at bridging this semantic gap embed images and text into a multimodal space, enabling the transfer of text-defined transformations to the image modality. We introduce the SIMAT dataset to evaluate the task of Image Retrieval with Multimodal queries. SIMAT contains 6k images and 18k textual transformation queries that aim at either replacing scene elements or changing pairwise relationships between scene elements. The goal is to retrieve an image consistent with the (source image, text transformation) query. We use an image/text matching oracle (OSCAR) to assess whether the image transformation is successful. The SIMAT dataset will be publicly available. We use SIMAT to evaluate the geometric properties of multimodal embedding spaces trained with an image/text matching objective, like CLIP. We show that vanilla CLIP embeddings are not very well suited to transform images with delta vectors, but that a simple finetuning on the COCO dataset can bring dramatic improvements. We also study whether it is beneficial to leverage pretrained universal sentence encoders (FastText, LASER and LaBSE).

关键词： computer vision conferences Semantics Image retrieval Lasers Transforms Image representation

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Pyramid Ensemble Structure for High Resolution Image Shadow Removal

Pyramid Ensemble Structure for High Resolution Image Shadow ...

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2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Cui, Shuhao Huang, Junshi Tian, Shuman Fan, Mingyuan Zhang, Jiaqi Zhu, Li Wei, Xiaoming Wei, Xiaolin Meituan Group China

ISBN: (纸本)9798350302493

Existing methods for shadow removal in high-resolution images may not be effective due to challenges such as the time-consuming nature of training and the loss of visual data during image cropping or resizing, highlighting the necessity for the development of more efficient methods. In this paper, we propose a novel Pyramid Ensemble Structure (PES) for High Resolution Image Shadow Removal. Our approach takes advantage of multiple scales by constructing pyramid inputs that allow for the capturing of a wide range of shadow sizes and shapes. We then train the network in pyramid stages to enhance global information processing. Furthermore, an ensemble of different shadow removal models is employed, and the maximum value is chosen to indicate the least amount of remaining shadow in the output. Experiments on both validation and testing data sets confirm the effectiveness of our method. In the Image Shadow Removal Challenge competition, our method obtained 22.36 PSNR score (1st place) and 0.70 SSIM score (2nd place) on the test sets. © 2023 ieee.

关键词： computer vision

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DeepSegmenter: Temporal Action Localization for Detecting Anomalies in Untrimmed Naturalistic Driving Videos

DeepSegmenter: Temporal Action Localization for Detecting An...

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2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Aboah, Armstrong Bagci, Ulas Mussah, Abdul Rashid Owor, Neema Jakisa Adu-Gyamfi, Yaw Northwestern University Department of Radiology United States University of Missouri-Columbia Department of Civil Engineering United States

ISBN: (纸本)9798350302493

Identifying unusual driving behaviors exhibited by drivers during driving is essential for understanding driver behavior and the underlying causes of crashes. Previous studies have primarily approached this problem as a classification task, assuming that naturalistic driving videos come discretized. However, both activity segmentation and classification are required for this task due to the continuous nature of naturalistic driving videos. The current study therefore departs from conventional approaches and introduces a novel methodological framework, DeepSegmenter, that simultaneously performs activity segmentation and classification in a single framework. The proposed framework consists of four major modules namely Data Module, Activity Segmentation Module, Classification Module and Postprocessing Module. Our proposed method won 8th place in the 2023 AI City Challenge, Track 3, with an activity overlap score of 0.5426 on experimental validation data. The experimental results demonstrate the effectiveness, efficiency, and robustness of the proposed system. The code is available at https://***/aboah1994/***. © 2023 ieee.

关键词： computer vision

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