DMesh++: An Efficient Differentiable Mesh for Complex Shapes
Sanghyun Son, Matheus Gadelha, Yang Zhou, Matthew Fisher, Zexiang Xu, Yi-Ling Qiao, Ming C. Lin, Yi Zhou
摘要
Point Cloud Recon. (b) 3D Point Cloud Recon. (b) 3D Multi View Recon.
Figure 1. DMesh++ for complex 2D and 3D shapes. DMesh++ encodes all geometric and topological information into continuous point features. (a) By optimizing these point features, DMesh++ is able to reconstruct complex 2D drawings from sample points. (b) This approach is also applicable to 3D, where it reconstructs the complex geometric structure of DNA from a point cloud. (c) By incorporating additional color features, DMesh++ can reconstruct complex, colored 3D shapes from multi-view images. For each result, the "imaginary" part is rendered in gray, while the "real" part-which defines the final mesh-is rendered in other colors. (Sec. 3.1).
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了最后一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper2
- MeshFlow: Efficient Artistic Mesh Generation via MeshVAE and Flow-based Diffusion TransformerWeiyu Li, Antoine Toisoul, Tom Monnier, Roman Shapovalov 等CVPR 2026 · 被引用 7 次
- ExMesh: EXplicit Mesh Reconstruction with Topology AdaptationChuanjin Fan, Lifan Wu, Wenjie Chang, Hanzhi Chang 等CVPR 2026 · 被引用 2 次
它引用的顶会 Paper22
- 3D Gaussian Splatting for Real-Time Radiance Field RenderingBernhard Kerbl, Georgios Kopanas, Thomas Leimkühler, George DrettakisSIGGRAPH 2023 · 被引用 5,687 次
- NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view ReconstructionPeng Wang, Lingjie Liu, Yuan Liu, Christian Theobalt 等NeurIPS 2021 · 被引用 2,500 次
- Volume Rendering of Neural Implicit SurfacesLior Yariv, Jiatao Gu, Yoni Kasten, Yaron LipmanNeurIPS 2021 · 被引用 1,421 次
- UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View ReconstructionMichael Oechsle, Songyou Peng, Andreas GeigerICCV 2021 · 被引用 885 次
- Soft Rasterizer: A Differentiable Renderer for Image-Based 3D ReasoningShichen Liu, Weikai Chen, Tianye Li, Hao LiICCV 2019 · 被引用 789 次
相关 Paper
- DMesh: A Differentiable Mesh RepresentationSanghyun Son, Matheus Gadelha, Yang Zhou, Zexiang Xu 等NeurIPS 2024 · 被引用 11 次
- Point2CAD: Reverse Engineering CAD Models from 3D Point CloudsYujia Liu, Anton Obukhov, Jan Dirk Wegner, Konrad SchindlerCVPR 2024
- Point2Mesh: a self-prior for deformable meshesRana Hanocka, Gal Metzer, Raja Giryes, Daniel Cohen-OrSIGGRAPH 2020 · 被引用 243 次
- DC-GNet: Deep Mesh Relation Capturing Graph Convolution Network for 3D Human Shape ReconstructionShihao Zhou, Mengxi Jiang, Shanshan Cai, Yunqi LeiACM MM 2021 · 被引用 14 次
- Neural Template: Topology-aware Reconstruction and Disentangled Generation of 3D MeshesKa-Hei Hui, Ruihui Li, Jingyu Hu, Chi-Wing FuCVPR 2022 · 被引用 24 次
