45 research outputs found

    Phase-Shifting Coder: Predicting Accurate Orientation in Oriented Object Detection

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    With the vigorous development of computer vision, oriented object detection has gradually been featured. In this paper, a novel differentiable angle coder named phase-shifting coder (PSC) is proposed to accurately predict the orientation of objects, along with a dual-frequency version (PSCD). By mapping the rotational periodicity of different cycles into the phase of different frequencies, we provide a unified framework for various periodic fuzzy problems caused by rotational symmetry in oriented object detection. Upon such a framework, common problems in oriented object detection such as boundary discontinuity and square-like problems are elegantly solved in a unified form. Visual analysis and experiments on three datasets prove the effectiveness and the potentiality of our approach. When facing scenarios requiring high-quality bounding boxes, the proposed methods are expected to give a competitive performance. The codes are publicly available at https://github.com/open-mmlab/mmrotate.Comment: Accepted to CVPR 2023, 10 pages, 4 figure

    Dual-Side Feature Fusion 3D Pose Transfer

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    3D pose transfer solves the problem of additional input and correspondence of traditional deformation transfer, only the source and target meshes need to be input, and the pose of the source mesh can be transferred to the target mesh. Some lightweight methods proposed in recent years consume less memory but cause spikes and distortions for some unseen poses, while others are costly in training due to the inclusion of large matrix multiplication and adversarial networks. In addition, the meshes with different numbers of vertices also increase the difficulty of pose transfer. In this work, we propose a Dual-Side Feature Fusion Pose Transfer Network to improve the pose transfer accuracy of the lightweight method. Our method takes the pose features as one of the side inputs to the decoding network and fuses them into the target mesh layer by layer at multiple scales. Our proposed Feature Fusion Adaptive Instance Normalization has the characteristic of having two side input channels that fuse pose features and identity features as denormalization parameters, thus enhancing the pose transfer capability of the network. Extensive experimental results show that our proposed method has stronger pose transfer capability than state-of-the-art methods while maintaining a lightweight network structure, and can converge faster

    H2RBox-v2: Boosting HBox-supervised Oriented Object Detection via Symmetric Learning

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    With the increasing demand for oriented object detection e.g. in autonomous driving and remote sensing, the oriented annotation has become a labor-intensive work. To make full use of existing horizontally annotated datasets and reduce the annotation cost, a weakly-supervised detector H2RBox for learning the rotated box (RBox) from the horizontal box (HBox) has been proposed and received great attention. This paper presents a new version, H2RBox-v2, to further bridge the gap between HBox-supervised and RBox-supervised oriented object detection. While exploiting axisymmetry via flipping and rotating consistencies is available through our theoretical analysis, H2RBox-v2, using a weakly-supervised branch similar to H2RBox, is embedded with a novel self-supervised branch that learns orientations from the symmetry inherent in the image of objects. Complemented by modules to cope with peripheral issues, e.g. angular periodicity, a stable and effective solution is achieved. To our knowledge, H2RBox-v2 is the first symmetry-supervised paradigm for oriented object detection. Compared to H2RBox, our method is less susceptible to low annotation quality and insufficient training data, which in such cases is expected to give a competitive performance much closer to fully-supervised oriented object detectors. Specifically, the performance comparison between H2RBox-v2 and Rotated FCOS on DOTA-v1.0/1.5/2.0 is 72.31%/64.76%/50.33% vs. 72.44%/64.53%/51.77%, 89.66% vs. 88.99% on HRSC, and 42.27% vs. 41.25% on FAIR1M.Comment: 13 pages, 4 figures, 7 tables, the source code is available at https://github.com/open-mmlab/mmrotat

    Regenerated Phase-shifted Sinusoid-assisted Empirical Mode Decomposition

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    The effectiveness of the renowned empirical mode decomposition (EMD) is affected by the mode-mixing problem (MMP) if a signal contains intermittent modes. The ensemble EMD (EEMD) and several modified and extended algorithms solve this problem by adding random white noises. However, the necessary large size of the ensemble and the inevitable manual intervention limits the application of EEMD. In this letter, a novel regenerated phase-shifted sinusoid-assisted EMD (RPSEMD) is proposed. Sinusoids with different scales are iteratively generated and added to cope with all possible MMPs in different intrinsic modes (IMs), where each sinusoid is designed adaptively and automatically. Furthermore, the sinusoids are shifted for better retaining the details of each IM and eliminating the added sinusoids. In the comparison experiments, the RPSEMD provides more reasonable results with less computation time.Accepted Versio

    Regenerated phase-shifted sinusoids assisted EMD for adaptive analysis of fringe patterns

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    Fringe patterns are often produced from optical metrology. It is important yet challenging to reduce noise and remove a complicated background in a fringe pattern, for which empirical mode decomposition based methods have been proven useful. However, the mode-mixing problem and the difficulty in automatic mode classification limit the application of these methods. In this paper, a newly developed method named regenerated phase-shifted sinusoids assisted empirical mode decomposition is introduced to decompose a fringe pattern, and subsequently, a new noise-signal-background classification strategy is proposed. The former avoids the mode-mixing problem appearing during the decomposition, while the latter adaptively classifies the decomposition results to remove the noise and background. The proposed method is testified by both simulation and real experiments, which shows effective and robust for fringe pattern analysis under different noise, fringe modulation, and defects.NRF (Natl Research Foundation, S’pore

    Sparse ICP With Resampling and Denoising for 3D Face Verification

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    Application of EMD in fringe analysis: new developments

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    Empirical mode decomposition (EMD) based methods have been widely used in fringe pattern analysis, including denoising, detrending, normalization, etc. The common problem of using EMD and Bi-dimensional EMD is the mode mixing problem, which is generally caused by uneven distribution of extrema. In recent years, we have proposed some algorithms to solve the mode mixing problem and further applied these methods in fringe analysis. In this paper, we introduce the development of these methods and show the successful results of two most recent algorithms.NRF (Natl Research Foundation, S’pore)Published versio

    Phase retrieval for high-speed 3D measurement using binary patterns with projector defocusing

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    Recent digital technology allows binary patterns to be projected with a very high speed, which shows great potential for high-speed 3D measurement. However, how to retrieve an accurate phase with an even faster speed is still challenging. In this paper, an accurate and efficient phase retrieval technique is presented, which combines a Hilbert three-step phaseshifting algorithm with a ternary Gray code-based phase unwrapping method. The Hilbert three-step algorithm uses three squared binary patterns, which can calculate an accurate phase even under a slight defocusing level. The ternary Gray code-based method uses four binary patterns, which can unwrap a phase with a large number of fringe periods. Both simulations and experiments have validated its accuracy and efficiency.NRF (Natl Research Foundation, S’pore)Published versio
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