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3D Trajectory Reconstruction of Moving Points Based on a Monocular Camera

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arxiv 2502.19689 v1 pith:NATQ3IDP submitted 2025-02-27 cs.CV

classification cs.CV
keywords cameramonocularmotionobservationpointspolynomialsproposedtemporal
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The motion measurement of point targets constitutes a fundamental problem in photogrammetry, with extensive applications across various engineering domains. Reconstructing a point's 3D motion just from the images captured by only a monocular camera is unfeasible without prior assumptions. Under limited observation conditions such as insufficient observations, long distance, and high observation error of platform, the least squares estimation faces the issue of ill-conditioning. This paper presents an algorithm for reconstructing 3D trajectories of moving points using a monocular camera. The motion of the points is represented through temporal polynomials. Ridge estimation is introduced to mitigate the issues of ill-conditioning caused by limited observation conditions. Then, an automatic algorithm for determining the order of the temporal polynomials is proposed. Furthermore, the definition of reconstructability for temporal polynomials is proposed to describe the reconstruction accuracy quantitatively. The simulated and real-world experimental results demonstrate the feasibility, accuracy, and efficiency of the proposed method.

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  1. Spiking Neural Networks with Temporal Attention-Guided Adaptive Fusion for imbalanced Multi-modal Learning

    cs.LG 2025-05 conditional novelty 5.0 of 10

    A temporal attention-guided fusion module plus attention-modulated loss raises multimodal SNN accuracy to 77.55% on CREMA-D, 70.65% on AVE, and 97.5% on EAD.

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