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针对在遮挡和日常环境中, 盒式物体位姿计算精度差等问题, 提出采用基于RTMPose-BRNM的盒式物体2D关键点检测和点云深度信息相结合的位姿计算方法. 首先, 引入RFAConv替换普通Conv卷积, 提高遮挡2D关键点坐标识别精确度; 使用NATTEN模块, 提高模型对盒式物体边缘轮廓点抽取能力; 设计混合感受野卷积(mixed-perception convolution, MPC)结构, 增强不同尺寸盒式物体识别适应性. 实验结果表明, RTMPose-BRNM关键点识别算法平均像素距离误差(mean pixel distance error, MPDE)为0.98, 相比于原RTMPose模型, 降低了1.19; 改进后平移误差和旋转误差为1.32%和0.96°左右.… … 相似文献
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吉布斯采样的临界行为是计算相变理论所关注的核心问题. 以硬核模型这一经典模型为例, 研究了吉布斯采样在临界点前的快速收敛. 在该模型中, 给定一个最大度为Δ≥3的n顶点图G以及参数λ≥0, 则图G中的每个独立集S以正比于λ|S|的概率被采样. 研究了实现这一采样的经典吉布斯采样算法——Glauber dynamics, 在临界条件λ<(Δ–1)Δ–1/(Δ–2)Δ下, 证明了该采样过程的马尔可夫链具有渐进最优的谱隙为Ω(1/n), 因此这一经典采样算法在该临界点前始终快速收敛.吉布斯采样过程在临界点前的快速收敛是马尔可夫链蒙特卡洛(MCMC) 理论中的一类重要问题. 针对硬核模型上的这一问题, 此前已有若干依赖高等数学工具的证明. 为这个重要问题提供了一个简化的组合证明, 引入计算复杂性归约的思想来分析采样过程的收敛速率.… … 相似文献
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针对密度峰值聚类（DPC）存在的截断距离参数需人工设置、对噪声敏感以及难以适应非均匀数据分布等问题,设计了牛顿-拉夫逊优化驱动的自适应密度峰值聚类方法（NRO-ADPC）。该方法基于牛顿-拉夫逊优化算法建立自适应参数优化机制,通过梯度引导的二阶优化自动确定截断距离,消除人工参数调整依赖;通过构建融合密度连续性、聚类分离度和峰值清晰度的多目标优化函数,结合自适应加权机制增强算法对噪声的鲁棒性;采用结合固定与自适应带宽的混合密度估计策略,在保持全局一致性的同时适应局部分布变化。在5个合成数据集和5个真实数据集上的实验表明,NRO-ADPC在合成数据集上平均准确率超过99%,在真实数据集上相比传统DPC平均提升超过20%。Wilcoxon符号秩检验显示,NRO-ADPC在ACC和NMI指标上相对于MDPC+等对比算法具有统计显著性优势（P<0.05）,在处理高噪声、多尺度特征及非均匀分布等复杂数据时表现出色。… … 相似文献
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ObjectiveClothed human generation, which aims to recover the 3D geometry and texture of the human body from input data to generate accurate 3D human models, is a challenging problem in the fields of computer vision and computer graphics. The need for high-quality generations has become increasingly critical with the growing demand for realistic 3D human models in applications such as virtual reality and augmented reality. Traditional multiview generation methods, which are often expensive and impractical for everyday use, typically require specialized equipment to capture images from multiple viewpoints. By contrast, obtaining single-view images from the web is much easier than obtaining multiview images. Thus, single-view generation methods become more cost-effective than multiview generation methods, and the model creation process becomes simple. Given these advantages, we consider using a single view as input to recover the 3D model of a clothed human. However, single-view images lack comprehensive spatial information and structural details of occluded regions. Thus, recovering a complete 3D shape becomes difficult. As a result, existing methods based on implicit functions struggle to learn rear-view information effectively, thereby leading to overly smooth and unrealistic back regions in the generated 3D human model. Methods combining diffusion models show some potential in enhancing texture detail performance. However, most of these methods lack view consistency constraints, thereby making the full recovery of the local texture details of the human body difficult. Additionally, the absence of precise geometric constraints during the diffusion process causes discrepancies between the generated models and the true geometry, particularly when handling complex 3D structures. Existing methods typically assume a uniform point distribution across spatial regions by ignoring variations in the distribution of query points caused by differences in distance from the human body surface. This assumption makes adapting to the geometric complexity differences across various regions of the body difficult for these methods. As a result, these methods face limitations when generating the surfaces of loose clothing, which have complex and variable geometries. This study addresses these challenges by combining three mechanisms： pose diffusion priors generation, multiview consistency constraints, and adaptive geometry generation. This approach not only preserves the generative capabilities of the diffusion model but also introduces geometric constraints to ensure the accuracy of the generation. Furthermore, this method can generate high-quality 3D human models by incorporating the probability distribution of human body structure. This study proposes a generation method that integrates pose diffusion priors with multiview consistency.MethodThis study constructs a method for single-view clothed human generation. First, a human pose estimation algorithm is used to extract 25 key points, which are encoded into Gaussian heatmaps to achieve spatial continuity modeling. This approach enables the model to understand the spatial relationships around the key points. The Gaussian heatmaps, combined with the human mask and UV mapping, are used to construct a pose feature vector. This feature vector guides the denoising process of the latent diffusion model and generates 2D diffusion images for unseen viewpoints through an adaptive cross-attention mechanism. Second, after the normal information of the （skinned multi-person linear model expressive, SMPLX） human template estimated from the input image and the 2D diffusion image are fused, they are input into the cross-view normal consistency network, where the multiview consistency mechanism extracts the corresponding 3D spatial features for each viewpoint. Finally, the voxelized features of the SMPLX human template and the 3D spatial features are fused and input into the distribution prediction network for spatial occupancy probability estimation. The model can express geometric uncertainty at different spatial locations and sample from the learned probability distribution by learning the distribution parameters of each point. Then, the 3D features, voxelized features, and sampling results are input into the occupancy prediction network to achieve 3D clothed human generation. Our entire model is trained on the THuman2.0 （Tsinghua human 2.0 dataset） dataset, with 490 images being used for training and 21 images being used for testing. We tested the model on the CAPE （clothed auto-person encoding） dataset to evaluate the generalization ability of the model further. This dataset is divided into two subsets： CAPE fitted poses （CAPE-FP）, which contains 75 images used to assess the geometric generation accuracy of the method under simple poses, and CAPE nonfitted poses （CAPE-NFP）, which contains 75 images and focuses on evaluating the method’s adaptability to complex poses. The experiments are conducted on an NVIDIA GeForce RTX 3090 GPU, with a learning rate being set to 1 × 10⁻4 and a batch size of 2.ResultWe conducted experiments on the THuman2.0 and CAPE datasets and compared the single-view clothed human generation results with the results of six other methods. Chamfer distance （CD） is used to evaluate the overall geometric similarity of the 3D human body, and point-to-surface distance （P2S） is used to assess the geometric accuracy of the reconstructed surface. Both metrics perform well when their values are small. On the THuman2.0 dataset, the CD and P2S metrics of the single-view clothed human generation method were reduced by 6.27% and 5.74%, respectively, compared with those of the best-performing method. On the CAPE-FP and CAPE-NFP subsets, the CD and P2S of the single-view clothed human generation method performed better than those of the other comparison methods. On the entire CAPE dataset, the CD metric of the single-view clothed human generation method decreased by an average of 8.67%, and the P2S metric decreased by an average of 2.38%. Quantitative experiments show that our method has good generalization ability for unseen data and can effectively handle human generation tasks in complex poses. Inference efficiency comparison results show that the computational complexity of our method is lower than that of similar diffusion model methods. Experimental results indicate that combining pose diffusion priors and multiview consistency helps recover the texture details of the 3D human body, and adaptive geometry generation enables accurate recovery of complex clothing topologies.ConclusionThe single-view 3D clothed human generation method proposed in this paper, which combines pose diffusion priors and multiview consistency, effectively recovers the local details of the clothed human and accurately generates 3D human models with complex topological structures, such as rich wrinkle details and loose clothing.… … 相似文献
《中国图象图形学报》2026,31(4):1256-1271
217.
锥形束计算机断层扫描(Cone Beam Computed Tomography, CBCT)是一种广泛用于医学领域的成像技术.基于高斯泼溅的R2-GS方法使用3D高斯表示CBCT三维图像,展现了出色的重建质量和实时的渲染速度.然而,R2-GS方法在重建区域边缘存在暗化现象,影响… … 相似文献
陈盛款;丁国平;丁佳骏 《小型微型计算机系统》2026,(3)
218.
遥感图像描述技术是遥感领域的重要研究方向,能够对图像内容进行智能解析.然而,现有算法通常计算复杂度高、资源消耗大,难以在资源受限的终端或场合应用.为此,本文设计了一种轻量化遥感图像描述生成模型,旨在降低模型复杂度,同时保持描述生成的准确性.首先,在图像编码器中引入对比语言-图像预… … 相似文献
韦培键;唐振华;崔振雷 《小型微型计算机系统》2026,(3)
219.
步态识别作为一种远程生物特征识别技术,在医疗康复、刑侦侦查及社会治安等领域展现出广泛的应用前景.近年来,随着深度学习的快速发展,步态识别方法逐渐从传统的卷积神经网络(Convolutional Neural Network, CNN)转向更为先进的Transformer架构.尽管… … 相似文献
沈澍;王森;黄苏岩;张秉睿 《小型微型计算机系统》2026,(3)
220.
近年来,利用安卓恶意应用实施的新型网络犯罪呈上升态势,现有方法在恶意应用行为解析的全面性、准确性及隐藏行为检出等方面存在不足,无法满足新型涉网案件快速侦办、有效打击的实战需求.基于此,提出一种新型安卓恶意应用行为细粒度解析框架：首先反编译安卓应用程序,以函数调用图(Functio… … 相似文献
史嘉琦;徐国天 《小型微型计算机系统》2026,(3)
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网站版权所有：中国科学院软件研究所 京ICP备09084417号-9
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
