| vsitzmann/awesome-implicit-representations |
1,795 |
|
0 |
0 |
over 3 years ago |
0 |
|
6 |
mit |
|
| A curated list of resources on implicit neural representations. |
| ranahanocka/MeshCNN |
1,211 |
|
0 |
0 |
about 4 years ago |
0 |
|
76 |
mit |
Python |
| Convolutional Neural Network for 3D meshes in PyTorch |
| chrischoy/3D-R2N2 |
1,097 |
|
0 |
0 |
over 4 years ago |
0 |
|
34 |
mit |
Python |
| Single/multi view image(s) to voxel reconstruction using a recurrent neural network |
| daniilidis-group/neural_renderer |
940 |
|
0 |
0 |
about 4 years ago |
0 |
|
59 |
other |
Python |
| A PyTorch port of the Neural 3D Mesh Renderer |
| FORTH-ModelBasedTracker/MocapNET |
738 |
|
0 |
0 |
over 2 years ago |
0 |
|
16 |
other |
C++ |
| We present MocapNET, a real-time method that estimates the 3D human pose directly in the popular Bio Vision Hierarchy (BVH) format, given estimations of the 2D body joints originating from monocular color images. Our contributions include: (a) A novel and compact 2D pose NSRM representation. (b) A human body orientation classifier and an ensemble of orientation-tuned neural networks that regress the 3D human pose by also allowing for the decomposition of the body to an upper and lower kinematic hierarchy. This permits the recovery of the human pose even in the case of significant occlusions. (c) An efficient Inverse Kinematics solver that refines the neural-network-based solution providing 3D human pose estimations that are consistent with the limb sizes of a target person (if known). All the above yield a 33% accuracy improvement on the Human 3.6 Million (H3.6M) dataset compared to the baseline method (MocapNET) while maintaining real-time performance |
| astorfi/3D-convolutional-speaker-recognition |
634 |
|
0 |
0 |
about 6 years ago |
0 |
|
7 |
apache-2.0 |
Python |
| :speaker: Deep Learning & 3D Convolutional Neural Networks for Speaker Verification |
| eecn/Hyperspectral-Classification |
309 |
|
0 |
0 |
almost 4 years ago |
0 |
|
6 |
other |
Python |
| Hyperspectral-Classification Pytorch |
| nshaud/DeepHyperX |
216 |
|
0 |
0 |
almost 5 years ago |
0 |
|
21 |
other |
Python |
| Deep learning toolbox based on PyTorch for hyperspectral data classification. |
| shervinea/enzynet |
188 |
|
0 |
0 |
over 2 years ago |
0 |
|
9 |
mit |
Python |
| EnzyNet: enzyme classification using 3D convolutional neural networks on spatial representation |
| jibikbam/CNN-3D-images-Tensorflow |
176 |
|
0 |
0 |
over 6 years ago |
0 |
|
5 |
|
Python |
| 3D image classification using CNN (Convolutional Neural Network) |
