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BGDNet: Background-guided Indoor Panorama Depth Estimation

BGDNet: Background-guided Indoor Panorama Depth Estimation

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Jiajing Chen Zhiqiang Wan Manjunath Narayana Yuguang Li Will Hutchcroft Senem Velipasalar Sing Bing Kang Syracuse University Zillow Group

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Depth estimation from single perspective image has received significant attention in the past decade, whereas the same task applied to single panoramic image remains comparatively under-explored. Most existing depth estimation models for panoramic images imitate models proposed for perspective images, which take RGB images as input and output depth directly. However, as demonstrated by our experiments, model performance drops significantly when the training and testing datasets greatly differ, since they over-fit the training data. To address this issue, we propose a novel method, referred to as the Background-guided Network (BGDNet), for more robust and accurate depth estimation from indoor panoramic images. Different from existing models, our proposed BGDNet first infers the background depth, namely from walls, floor and ceiling, via background masks, room layout and camera model. The background depth is then used to guide and improve the output foreground depth. We perform within dataset as well as cross-domain experiments on two benchmark datasets. The results show that BGDNet outperforms the state-of-the-art baselines, and is more robust to overfitting issues, with superior generalization across datasets.

关键词： Training computer vision conferences Layout Estimation Training data Cameras

来源：评论

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Hand-Eye Calibration Based on Screw Motions

Hand-Eye Calibration Based on Screw Motions

引用

International conference on pattern recognition

作者： Zijian Zhao Yuncai Liu Institute of Image Processing & Pattern Recognition Shanghai Jiaotong University Shanghai China

When computer vision technique is used in robotics, robotic hand-eye calibration is a very important research task. Many algorithms have been proposed for hand-eye calibration. Based on these algorithms, we introduce a new hand-eye calibration algorithm in this paper, which employs the screw motion theory to establish a hand-eye matrix equation by using quaternion and gets a simultaneous result for rotation and translation by solving linear equations. The algorithm proposed in this paper has high and stable computational efficiency without non-linear minimization and can be understood easily. Both simulations and real experiments show the superiority of our algorithm over the comparative algorithms

关键词： Calibration Fasteners Robot vision systems Robot sensing systems Quaternions Iterative algorithms Cameras computer vision Nonlinear equations Minimization methods

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GM-DETR: Generalized Muiltispectral DEtection TRansformer with Efficient Fusion Encoder for Visible-Infrared Detection

GM-DETR: Generalized Muiltispectral DEtection TRansformer wi...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Yiming Xiao Fanman Meng Qingbo Wu Linfeng Xu Mingzhou He Hongliang Li University of Electronic Science and Technology of China China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Multispectral object detection based on RGB and IR achieves improved accurate and robust performance by integrating complementary information from different modalities. However, existing methods predominantly focus on effectively fusing information from both modalities to enhance detection performance, and rarely study the diversified utilization of RGB and IR data and explore the adaptability of the model to practical application scenarios. We first analyze the utilization of datasets for multispectral object detection, and compare their testing performance. To better leverage datasets and address more generalized model application scenarios, we propose a Generalized Multispectral DEtection TRansformer (GM-DETR) with a two-stage training strategy. Specifically, we design the Modality-Specific Feature Interaction (MSFI) module to extract the high-level information from RGB and IR, and propose the Cross-Modality-Scale feature Fusion (CMSF) module for fusing RGB and IR modalities, which performs multi-scale cross-modalities fusion. Our GM-DETR achieves state-of-the-art performance on FLIR and LLVIP benchmark datasets.

关键词： Training Adaptation models computer vision conferences Object detection Benchmark testing Feature extraction

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High Quality Reference Feature for Two Stage Bracketing Image Restoration and Enhancement

High Quality Reference Feature for Two Stage Bracketing Imag...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Xiaoxia Xing HyunHee Park Fan Wang Ying Zhang Sejun Song Changho Kim Xiangyu Kong Samsung R&D Institute China-Beijing Department of Camera Innovation Group Samsung Electronics

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In a low-light environment, it is difficult to obtain high-quality or high-resolution images with sharp details and high dynamic range (HDR) without noise or blur. To solve this problem, the Bracketing Image Restoration and Enhancement integrates Dnoise, Deblur, HDR Reconstruction, and Super Resolution techniques into a unified framework. However, we find that most methods select the image that aligns with GT as the reference image. Since the details of the reference image are not good enough, seriously affects the feature fusion, which finally leads to details being blurred. To generate a high dynamic range and a high-quality image, we propose a two-stage Bracketing method named RT-IRE. In the first stage, we generate the high-quality reference feature to guide feature fusion, remove the degradation, and reconstruct HDR to get coarse results. The second stage learns the residuals between the coarse result and the GT, which further enhances and generates details. Extensive experiments show the effectiveness of the proposed module. In particular, RT-IRE won two champions in the NTIRE 2024 Bracketing Image Restoration and Enhancement Challenge.

关键词： Degradation computer vision Accuracy conferences Noise Superresolution Image restoration

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Multi-Modal Fusion of Event and RGB for Monocular Depth Estimation Using a Unified Transformer-based Architecture

Multi-Modal Fusion of Event and RGB for Monocular Depth Esti...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Anusha Devulapally Md Fahim Faysal Khan Siddharth Advani Vijaykrishnan Narayanan The Pennsylvania State University Samsung Electronics America

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In the field of robotics and autonomous navigation, accurate pixel-level depth estimation has gained significant importance. Event cameras or dynamic vision sensors, capture asynchronous changes in brightness at the pixel level, offering benefits such as high temporal resolution, no motion blur, and a wide dynamic range. However, unlike traditional cameras that measure absolute intensity, event cameras lack the ability to provide scene context. Efficiently combining the advantages of both asynchronous events and synchronous RGB images to enhance depth estimation remains a challenge. In our study, we introduce a unified transformer that combines both event and RGB modalities to achieve precise depth prediction. In contrast to individual transformers for input modalities, a unified transformer model captures inter-modal dependencies and uses self-attention to enhance event-RGB contextual interactions. This approach exceeds the performance of recurrent neural network (RNN) methods used in state-of-the-art models. To encode the temporal information from events, convLSTMs are used before the transformer to improve depth estimation. Our proposed architecture outperforms the existing approaches in terms of absolute mean depth error, achieving state-of-the-art results in most cases. Additionally, the performance is also seen in other metrics like RMSE, absolute relative difference and depth thresholds compared to the existing approaches. The source code is available at: https://***/anusha-devulapally/ER-F2D.

关键词： Measurement computer vision Accuracy Recurrent neural networks Robot vision systems Estimation computer architecture

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Robust Visual Tracking Using Case-Based Reasoning with Confidence

Robust Visual Tracking Using Case-Based Reasoning with Confi...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Zhiwei Zhu Wenhui Liao Qiang Ji Department of ECSE Rensselaer Polytechnic Institute Troy NY USA Sarnoff Corporation USA

The paper describes a simple but robust framework for visual object tracking in a video sequence. Compared with the existing tracking techniques, our proposed tracking technique has two significant contributions. First, a Case- Based Reasoning (CBR) paradigm is introduced to track the non-rigid object robustly under significant appearance changes without drifting away. Second, it can provide an accurate confidence measurement for each tracked object so that the tracking failures can be identified successfully. Specifically, under this framework, the appearance changes of the object being tracked can be adapted dynamically during tracking via an adaption mechanism of CBR. Hence, an accurate 2D tracking model can be maintained online for each image frame during tracking. Therefore, the proposed tracking technique possesses a self-recovery capability so that the object can be tracked robustly under significant appearance changes without error accumulation. Application was focused on the development of a real-time face tracking system. Via the proposed framework, the built real-time face tracker can track the human face robustly at 26 frames per second under various face orientations, significant facial expression and external illumination changes.

关键词： Robustness Video sequences Face Cameras Geometry Real time systems Humans Lighting computer Society computer vision

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Multi-Level Feature Fusion Network for Lightweight Stereo Image Super-Resolution

Multi-Level Feature Fusion Network for Lightweight Stereo Im...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Yunxiang Li Wenbin Zou Qiaomu Wei Feng Huang Jing Wu Fuzhou University South China University of Technology Chengdu University of Information Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Stereo image super-resolution utilizes the cross-view complementary information brought by the disparity effect of left and right perspective images to reconstruct higher-quality images. Cascading feature extraction modules and cross-view feature interaction modules to make use of the information from stereo images is the focus of numerous methods. However, this adds a great deal of network parameters and structural redundancy. To facilitate the application of stereo image super-resolution in downstream tasks, we propose an efficient Multi-Level Feature Fusion Network for Lightweight Stereo Image Super-Resolution (MFFSSR). Specifically, MFFSSR utilizes the Hybrid Attention Feature Extraction Block (HAFEB) to extract multi-level intra-view features. Using the channel separation strategy, HAFEB can efficiently interact with the embedded cross-view interaction module. This structural configuration can efficiently mine features inside the view while improving the efficiency of cross-view information sharing. Hence, reconstruct image details and textures more accurately. Abundant experiments demonstrate the effectiveness of MFFSSR. We achieve superior performance with fewer parameters. The source code is available at https://***/KarosLYX/MFFSSR.

关键词： computer vision Source coding conferences Computational modeling Superresolution Redundancy Information sharing

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3D Human Pose Estimation with Occlusions: Introducing BlendMimic3D Dataset and GCN Refinement

3D Human Pose Estimation with Occlusions: Introducing BlendM...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Filipa Lino Carlos Santiago Manuel Marques LARSyS Instituto Superior Técnico Institute for Systems and Robotics Portugal

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In the field of 3D Human Pose Estimation (HPE), accurately estimating human pose, especially in scenarios with occlusions, is a significant challenge. This work identifies and addresses a gap in the current state of the art in 3D HPE concerning the scarcity of data and strategies for handling occlusions. We introduce our novel BlendMimic3D dataset, designed to mimic real-world situations where occlusions occur for seamless integration in 3D HPE algorithms. Additionally, we propose a 3D pose refinement block, employing a Graph Convolutional Network (GCN) to enhance pose representation through a graph model. This GCN block acts as a plug-and-play solution, adaptable to various 3D HPE frameworks without requiring retraining them. By training the GCN with occluded data from Blend-Mimic3D, we demonstrate significant improvements in resolving occluded poses, with comparable results for non-occluded ones. Project web page is available at https://***/BlendMimic3D/.

关键词： Training Solid modeling computer vision Three-dimensional displays Graph convolutional networks conferences Pose estimation

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Constraint-Aware Importance Estimation for Global Filter Pruning under Multiple Resource Constraints

Constraint-Aware Importance Estimation for Global Filter Pru...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Yu-Cheng Wu Chih-Ting Liu Bo-Ying Chen Shao-Yi Chien NTU IoX Center National Taiwan University

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Filter pruning is an efficient way to structurally remove the redundant parameters in convolutional neural network, where at the same time reduces the computation, memory storage and transfer cost. Recent state-of-the-art methods globally estimate the importance of each filter based on its impact to the loss and iteratively remove those with smaller values until the pruned network meets some resource constraints, such as the commonly used number (or ratio) of filter left. However, when there is a more practical constraint like the total number of FLOPs, they ignore its relation to the estimation of filter importance. We propose a novel method called Constraint-Aware Importance Estimation (CAIE) that integrates information of the impact on the given resource into the original importance estimation only based on loss when pruning each filter. Moreover, our CAIE can be generalized to the pruning problem under multiple resource constraints simultaneously. Extensive experiments show that under the same multiple resource constraints, the model pruned with our CAIE method can not only accurately meet the constraints but also achieve the optimal performance results when comparing to existing state-of-the-art methods.

关键词： Estimation Computational modeling Training Optimization Performance evaluation Taylor series computer vision

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Adapting the Segment Anything Model During Usage in Novel Situations

Adapting the Segment Anything Model During Usage in Novel Si...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Robin Schön Julian Lorenz Katja Ludwig Rainer Lienhart Universität Augsburg Augsburg

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The interactive segmentation task consists in the creation of object segmentation masks based on user interactions. The most common way to guide a model towards producing a correct segmentation consists in clicks on the object and background. The recently published Segment Anything Model (SAM) supports a generalized version of the interactive segmentation problem and has been trained on an object segmentation dataset which contains 1.1B masks. Though being trained extensively and with the explicit purpose of serving as a foundation model, we show significant limitations of SAM when being applied for interactive segmentation on novel domains or object types. On the used datasets, SAM displays a failure rate FR 30 @90 of up to 72.6%. Since we still want such foundation models to be immediately applicable, we present a framework that can adapt SAM during immediate usage. For this we will leverage the user interactions and masks, which are constructed during the interactive segmentation process. We use this information to generate pseudo-labels, which we use to compute a loss function and optimize a part of the SAM model. The presented method causes a relative reduction of up to 48.1% in the FR 20 @85 and 46.6% in the FR 30 @90 metrics.

关键词： Measurement Adaptation models Image segmentation computer vision Computational modeling conferences Object segmentation

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