High Fidelity Semantic Shape Completion for Point Clouds using Latent Optimization
题目：利用潜在优化方法完成点云的高保真语义的形状补全 摘要 在3D计算机视觉中语义形状补全是一个具有挑战性的问题，这个任务是使用一个部分3D形状作为输入产生一个完整的3D形状。作者提出了一个基于学习的方法通过生成模型和潜在的流形优化来补全不完整的3D形状。作者的算法工作直接作用与点云上。作者使用一个自编码器和一个GAN网络来学习对象类点云的嵌入分布。首先将缺失区域的输入点云编码为特征向量，然后使用
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相关文章
- 1. Learning Oracle Attention for High-fidelity Face Completion
- 2. Generative Adversarial Network Fitting for High Fidelity 3D Face Reconstruction
- 3. KPConv: Flexible and Deformable Convolution for Point Clouds
- 4. Point Cloud 2019
- 5. 【论文速递】PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation
- 6. 【论文阅读】SSN: Shape Signature Networks for Multi-class Object Detection from Point Clouds
- 7. 点云补洞算法+代码（Shape Controllable geometry completion for point cloud models）
- 8. LARGE SCALE GAN TRAINING FOR HIGH FIDELITY NATURAL IMAGE SYNTHESIS
- 9. UIST '16 paper NormalTouch and TextureTouch: High-fidelity 3D Haptic Shape Rendering on Handheld
- 10. 3D Point Clouds-1