Sparse voxel convolutions are permutation invariant because convolution depends on coordinates and random access, not contiguous storage.
neural-networkssparse-convolution
The Neural Radiance Fields (NeRF) proposed an interesting way to represent a 3D scene using an implicit network for high fidelity volumetric rendering. Compared with traditional...
neural-networksrendering3d
Abstract The automation of mechanical tasks brought the modern world unprecedented prosperity and comfort. However, the majority of automated tasks have been simple mechanical ...
3d-perceptionneural-networkscomputer-vision
Authors Christopher Choy, Jaesik Park, Vladlen Koltun International Conference on Computer Vision (ICCV), 2019 Speed vs. accuracy Pareto frontier of prior methods and FCGF....
representation-learning3d-featurescomputer-vision
We have heard enough about the great success of neural networks and how they are used in real problems. Today, I want to talk about how it was so successful (partially) from an ...
data-processing-inequalityinformation-theoryneural-networks
Abstract 3D semantic scene labeling is fundamental to agents operating in the real world. In particular, labeling raw 3D point sets from sensors provides fine-grained semantics...
3d-vision3d-semantic-segmentationneural-networks
We present a deep learning framework for accurate visual correspondences and demonstrate its effectiveness for both geometric and semantic matching, spanning across rigid motion...
correspondenceneural-networksfully-convolutional-neural-network
Computing the neural network gradient requires very simple calculus, yet can be tedious. Affine Transformation (Fully Connected Layer) Gradients For a simple fully connected l...
back-propagationneural-networks