1.This week I found a paper, SDM-NET: Deep Generative Network for Structured Deformable Mesh. Their main idea is to decompose a 3D object into parts. And each part can be deformed from a homeomorphic simple object. Their network can also learn the support and symmetry information from the input. The part structures and geometries are …
Paper reading: SDM-NET & New tools for 3D deep learning Read More »
Last week I finish the function for the pose skeleton normalization. Given a referenced skeleton expressed with global coordinates, we can take some joints of it(instead of all joints) and normalize the length of the chosen joints of the skeletons in other frames. All the computations are using PyTorch’s tensor, which can be easily used …
Last week I helped Zhenyi install the Ubuntu 18.04 dual system, and install NVIDIA driver, CUDA-10.0, CUDNN, Pytorch-gpu. Although there are many tutorials on the Internet, only very few works. So I think it is better to make a record. BIOS setting Shutdown and reboot. And enter the BIOS interface. Disable the Secure Boot. If …
Kristofer and I found a paper Learning Implicit Fields for Generative Shape Modeling [https://arxiv.org/pdf/1812.02822.pdf], which is about using the implicit function to do 3D reconstruction. They use a DenseNet-like structure. Unlike DeepSDF, they use a binary output. Given a latent vector and a 3D coordinate, the network will output 0/1 which expresses outside/inside. Its structure …
Description As a combination of Computer Graphics and Computer Vision, 3D reconstruction has been a classic problem for a long time. In recent years, with the development of Deep Learning, more and more researchers are focusing on 3D reconstruction with Deep Learning again. Related Work The number of work focus on 3D reconstruction is increasing …
A brief overview of single-view 3D reconstruction based on Deep Learning Read More »
