Sparse 3DMatch Benchmark. Running Time. 4.3. Generalization to Outdoor Scenes 4.4. Ablation Study Descriptor Dimension & Viewpoint Number. Viewpoints. Multi-view Fusion. 5. Conclusion 我们提出了一种新颖的端到端框架,用于学习 3D 点云的局部多视图描述符。我们的框架执行具有可优化视点的网络内多视图渲...
Learningmultiview3Dpointcloudregistration ZanGojcic ∗§ CaifaZhou ∗§ JanD.Wegner § LeonidasJ.Guibas † TolgaBirdal † § ETHZurich † StanfordUniversity Abstract Wepresentanovel,end-to-endlearnable,multiview3D pointcloudregistrationalgorithm.Registrationofmultiple scanstypicallyfollowsatwo-stage...
Multiview CNNs:尝试将 3D point cloud或形状渲染为 2D 图像,然后应用 2D 卷积网络对它们进行分类;借助精心设计的图像 CNN,这一系列方法在shape classification(形状分类)和 retrieval tasks(检索任务)中取得了主导性能 ;但是将它们扩展到场景理解或其他 3D 任务(例如 point classification 点分类和 shape completion ...
[CVPR2019] 3D Local Features for Direct Pairwise Registration. [ICCV2019] Robust Variational Bayesian Point Set Registration. [ICRA2019] Robust low-overlap 3-D point cloud registration for outlier rejection. Learning multiview 3D point cloud registration[CVPR2020]点...
DSplats: 3D Generation by Denoising Splats-Based Multiview Diffusion Models content type paper | research area Computer Vision | Published year 2025 AuthorsKevin Miao, Harsh Agrawal, Qihang Zhang, Federico Semeraro, Marco Cavallo, Jiatao Gu, Alexander Toshev On the Modeling Capabilities of Large Lan...
这类方法将3D点云投影至不同的表示模态(多视角,体素表示等)来进行特征学习和形状分类。 2.1.1 多视角表示 这类方法首先将3D物体映射至多种视角,并且提取出对应的view-wise特征,将这些特征融合后进行准确的物体识别。如何将多视角的特征融合成一个判别的全局表示是主要的挑战。MVCNN[15]将多视角的特征进行最大...
In this work, we propose an end-to-end framework to learn local multi-view descriptors for 3D point clouds. To adopt a similar multi-view representation, existing studies use hand-crafted viewpoints for rendering in a preprocessing stage, which is detached from the subsequent descriptor learning ...
Machine Learning All research Evaluating Sample Utility for Data Selection by Mimicking Model Weights content typepaper|research areaComputer Vision,research areaData Science and Annotation|Published year2025 AuthorsTzu-Heng Huang, Manjot Bilkhu, Frederic Sala, Javier Movellan ...
To the best of our knowledge, this is the first deep multi-modal 3D shape clustering method; (2) By simultaneously ensuring the representation consistency within multi-view modality and between point cloud and multi-view modalities, a representation-level dual contrastive learning module is proposed...
一个3D CNN流和一些2D CNN流用来提取特征,另一个可微分的back-projection layer用来合并3D和2D特征。更进一步,[168]提出了unified point-based network来学习2D纹理信息,3D结构和全局特征。该方法直接应用基于点的网络来提取局部几何特征和环境信息。[159]提出了Multiview PointNet(MVPNet)来集成2D多视角特征和空间...