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针对复杂室内场景中经典三维重建方法存在细节表征不足、边缘模糊、混叠伪影等问题,提出一种基于三维高斯溅射（3DGS）的复杂室内场景三维重建算法。首先提出前置点云增密网络对运动结构恢复的稀疏点云进行增密补全,构建高分辨率的增强3D高斯辐射场,提高对复杂场景细节部分的表征精度;随后引入… … 相似文献
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借助超声成像设备可以对发音生理舌体进行动态记录,超声舌成像的处理分析对于实践语言学、实验语音学研究以及语音工程应用具有重要意义,其中,舌体运动轨迹动态分析和舌体轮廓线的舌位姿态量化分析是实际应用中的关键技术。由于超声舌成像模糊、舌轮廓不清晰等噪声特性,其处理分析面临着舌位特征提取… … 相似文献
张金溪;张克宏;于洪志 《中国图象图形学报》2026,31(2):391-432
448.
Objective3D single-object tracking （3D SOT） is of paramount importance in a wide array of applications, including autonomous driving, robotics, and intelligent security. The fundamental goal of 3D SOT is to localize a specific target across a sequence of point clouds, with the only given information being its initial status. Existing matching-based 3D SOT methods generally utilize certain forms of Siamese networks for feature extraction. After transforming the cropped target template and search area embeddings to the same feature space with a shared encoder, these methods enhance target-specific features with various appearance matching techniques, such as cosine similarity and cross attention. Although the Siamese matching-based paradigm has become a popular design in existing models, appearance matching has long suffered from issues with textureless and incomplete LiDAR point clouds. Beyond this paradigm, a new motion-centric tracker, M2-Track, offers a new perspective for 3D SOT. It takes point clouds from two successive frames without cropping as input, explicitly modeling the relative target motion in a single-stream architecture, largely overcoming the challenges. However, it fuses adjacent point clouds and processes them in a single-stream architecture, lacking explicit target information from adjacent frames for accurate localization. To compensate for this deficiency, M2-Track requires additional segmentation and box refinement, which makes the training objective complex and results in cumulative errors. To this end, this study proposes a novel Siamese motion-centric tracking approach, dubbed SiamMo.MethodSiamMo adopts a simple single-stage tracking pipeline of Siamese feature extraction and motion modeling. To learn excellent features for point clouds of varying density, we first divide nonuniform points into regular voxels, which exhibit reduced sensitivity to point count variations, thus mitigating the varying sparsity issue to some extent. Afterward, we present a top-down convolutional network based on Siamese architecture to encode voxelized point clouds of successive frames into the same feature space. The network first uses sparse convolution to extract features in 3D space and then adopts dense convolution to extract features in 2D space in a bird’s eye view. In contrast with the single-steam architecture of M2-Track, Siamese architecture decouples feature extraction from temporal fusion, which enables it to extract more abundant and representative latent features while reducing information interference among successive frames. Subsequently, we design a spatiotemporal feature aggregation （STFA） module that integrates the encoded features at multiple scales for motion modeling. Intuitively, effective motion modeling necessitates rich representations at multiple scales. Integrating these multifaceted representations is expected to significantly enhance the network’s capability to accurately localize targets with various motion patterns. Moreover, we introduce a box-aware feature encoding （BFE） module that injects explicit box priors into motion features for prediction. It first encodes the bounding box size parameters of the object in the initial frame to the box-aware encoding with a multilayer perception （MLP）. Then, BFE adds the box-aware encoding and the output feature from the STFA module. Finally, the added feature is fed into an MLP to regress the relative target motion. Despite being conceptually simple, our BFE can boost tracking performance, with negligible computation. In a nutshell, using neither segmentation nor box refinement, SiamMo achieves precise localization by directly inferring the relative target motion in a single-stage manner.ResultWe compare our model with several state-of-the-art tracking methods, including Siamese matching-based and motion-centric trackers on three public datasets, namely, Kitti, NuScenes, and WOD. The quantitative evaluation metrics comprise Precision and Success. Experimental results show that our model outperforms all other methods on Kitti, NuScenes, and WOD datasets. On the Kitti dataset, compared with the second-ranked method, our method increases the average success indicator by 4.7% and the average precision indicator by 4.9%. On the NuScenes dataset, the average success indicator is increased by 14.2%, and the average precision indicator is increased by 11.5%. On the WOD dataset, the average success indicator is increased by 2.9%, and the average precision indicator is increased by 5.4%. Our method also demonstrates strong robustness to sparsity and distractors on the difficult test subsets of the Kitti and NuScenes datasets. In addition, we report the efficiency of SiamMo, which is lightweight with only 0.82 GFLOPs and 14.6 M parameters. We record the average running time of all test frames for the Car category on the Kitti dataset to evaluate the computational efficiency of our method, which achieves 108 frame/second, including 4.2 ms for pre/processing point clouds and 5.0 ms for network forward propagation on a single NVIDIA 4090 GPU. Ablation experiments conducted on the Kitti and NuScenes datasets further verify the effectiveness of the Siamese architecture, STFA module, and BFE module. In Kitti’s Car category tracking task, replacing the single-stream architecture with the Siamese architecture results in a 3.8% increase in the average success and precision. When STFA leverages features across all scales, Kitti’s Car success and precision are improved by 2.2% and 2.6%, respectively, as opposed to relying solely on single-scale features. When the BFE module is added, Kitti’s Car success and precision are improved by 2.9% and 3.6%, respectively.ConclusionIn this paper, we propose a novel and simple Siamese motion-centric tracking approach, which avoids the vulnerable appearance matching process and does not require additional presegmentation and box refinement by adopting Siamese architecture and models target motion in a simple single-stage pipeline. Comprehensive experiments demonstrate that our model surpasses state-of-the-art methods on three challenging benchmarks while demonstrating excellent robustness and maintaining a high inference speed.… … 相似文献
Yang Yuxiang;Deng Yingqi;Gu Hongjie;Dong Zhekang;Zhang Jing 《中国图象图形学报》2026,31(2):512-524
449.
ObjectiveAs critical components of a vibration reduction system, the primary and secondary coil springs of locomotives are vital to safe tra… … 相似文献
《中国图象图形学报》2026,31(2):499-511
450.
随着卫星通信技术的快速演进,星地融合网络已成为构建下一代全球网络的重要发展方向。低轨卫星作为其重要组成部分,以低延迟和广覆盖等优势,正成为全球产业界与学术界的焦点。低轨卫星在近地轨道上高速运动,导致星地融合网络拓扑结构的高动态变化,这使得星地和星间的频繁移动切换成为影响通信连续性… … 相似文献
付一阳;胡博;刘人鹏;陈山枝 《计算机学报》2026,(2)
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地址：北京海淀区中关村南四大街4号 邮编：100190 电话：010-62562563 E-mail ： cj_mail@jscas.ac.cn
本系统由:北京勤云科技发展有限公司提供技术支持
