使固定 发表于 2025-3-21 19:09:30
书目名称Computer Vision – ECCV 2022影响因子(影响力)<br> http://impactfactor.cn/if/?ISSN=BK0234256<br><br> <br><br>书目名称Computer Vision – ECCV 2022影响因子(影响力)学科排名<br> http://impactfactor.cn/ifr/?ISSN=BK0234256<br><br> <br><br>书目名称Computer Vision – ECCV 2022网络公开度<br> http://impactfactor.cn/at/?ISSN=BK0234256<br><br> <br><br>书目名称Computer Vision – ECCV 2022网络公开度学科排名<br> http://impactfactor.cn/atr/?ISSN=BK0234256<br><br> <br><br>书目名称Computer Vision – ECCV 2022被引频次<br> http://impactfactor.cn/tc/?ISSN=BK0234256<br><br> <br><br>书目名称Computer Vision – ECCV 2022被引频次学科排名<br> http://impactfactor.cn/tcr/?ISSN=BK0234256<br><br> <br><br>书目名称Computer Vision – ECCV 2022年度引用<br> http://impactfactor.cn/ii/?ISSN=BK0234256<br><br> <br><br>书目名称Computer Vision – ECCV 2022年度引用学科排名<br> http://impactfactor.cn/iir/?ISSN=BK0234256<br><br> <br><br>书目名称Computer Vision – ECCV 2022读者反馈<br> http://impactfactor.cn/5y/?ISSN=BK0234256<br><br> <br><br>书目名称Computer Vision – ECCV 2022读者反馈学科排名<br> http://impactfactor.cn/5yr/?ISSN=BK0234256<br><br> <br><br>红肿 发表于 2025-3-21 23:50:13
,ASpanFormer: Detector-Free Image Matching with Adaptive Span Transformer,and local granularity, we propose ASpanFormer, a Transformer-based detector-free matcher that is built on hierarchical attention structure, adopting a novel attention operation which is capable of adjusting attention span in a self-adaptive manner. To achieve this goal, first, flow maps are regresseFRONT 发表于 2025-3-22 02:19:47
,NDF: Neural Deformable Fields for Dynamic Human Modelling,sent a dynamic human body with shared canonical neural radiance fields which links to the observation space with deformation fields estimations. However, the learned canonical representation is static and the current design of the deformation fields is not able to represent large movements or detail使增至最大 发表于 2025-3-22 05:39:52
Neural Density-Distance Fields,) have been proposed to estimate distance or density fields using neural fields. However, it is difficult to achieve high localization performance by only density fields-based methods such as Neural Radiance Field (NeRF) since they do not provide density gradient in most empty regions. On the other构成 发表于 2025-3-22 12:39:09
,NeXT: Towards High Quality Neural Radiance Fields via Multi-skip Transformer, existing NeRF based methods, including its variants, treat each sample point individually as input, while ignoring the inherent relationships between adjacent sample points from the corresponding rays, thus hindering the reconstruction performance. To address this issue, we explore a brand new sche不能平静 发表于 2025-3-22 16:42:00
http://reply.papertrans.cn/24/2343/234256/234256_6.png不能平静 发表于 2025-3-22 19:17:23
,Decomposing the Tangent of Occluding Boundaries According to Curvatures and Torsions,en 3D occluding boundaries and their 2D image projections by radial curvature, planar curvature, and Gaussian curvature. Occluding boundaries have also been studied implicitly as intersections of level surfaces, avoiding their explicit description in terms of local surface geometry. In contrast, thiwhite-matter 发表于 2025-3-23 01:09:41
http://reply.papertrans.cn/24/2343/234256/234256_8.png细微的差异 发表于 2025-3-23 01:48:55
Generalizable Patch-Based Neural Rendering,w synthesis considerably. The recent focus has been on models that overfit to a single scene, and the few attempts to learn models that can synthesize novel views of unseen scenes mostly consist of combining deep convolutional features with a NeRF-like model. We propose a different paradigm, where nPatrimony 发表于 2025-3-23 07:22:06
,Improving RGB-D Point Cloud Registration by Learning Multi-scale Local Linear Transformation,is the key to its success. In addition to previous methods that seek correspondences by hand-crafted or learnt geometric features, recent point cloud registration methods have tried to apply RGB-D data to achieve more accurate correspondence. However, it is not trivial to effectively fuse the geomet