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- On-the-fly Point Feature Representation for Point Clouds Analysis13
This paper proposes On-the-fly Point Feature Representation (OPFR), which captures abundant geometric information explicitly through Curve Feature Generator module, and introduces the novel Hierarchical Sampling module aimed at enhancing the quality of triangle sets, thereby ensuring robustness of the obtained geometric features.
- Uncertainty Meets Diversity: A Comprehensive Active Learning Framework for Indoor 3D Object Detection10
This paper presents the first study on active learning for indoor 3D object detection, where a novel framework tailored for this task is proposed, and incorporates two key criteria - uncertainty and diversity - to actively select the most ambiguous and informative unlabeled samples for annotation.
- Spectral-Based SPD Matrix Representation for Signal Detection Using a Deep Neutral Network7
A spectral-based SPD matrix signal detection method with deep learning that uses time-frequency spectra to construct SPD matrices and then exploits a deep SPD matrix learning network to detect the target signal.
- Spectral Convolution Feature-Based SPD Matrix Representation for Signal Detection Using a Deep Neural Network4
A deep neural network signal detection method based on spectral convolution features is proposed, and, under low SCR (signal-to-clutter ratio), this method can obtain a gain of 0.5–2 dB on simulated data set and semi-physical simulated data sets.
- A signal detection method based on matrix information geometric dimensionality reduction3
Inspired by image processing techniques, the short time Fourier transform is used to generate sufficient 2-D spectrograms of the received data to construct high dimensional covariance matrices, transforming into a binary classification problem lying on a symmetric positive definite (SPD) manifold.
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- Improving 3D Occupancy Prediction through Class-Balancing Loss and Multi-Scale Representation1
This paper introduces a novel UNet-like Multi-scale Occupancy Head module, inspired by the success of UNet in semantic segmentation tasks, and proposes the class-balancing loss to compensate for rare classes in the dataset.
Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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