Volt argues that a vanilla transformer, with the right recipe and enough data, no longer needs a convolution-specialized backbone for 3D scene understanding. We took that seriou...
3dtransformerconvolution
Imagine asking a robot: “bring me the Snoopy plush from the corner of the room.” For this to work, the robot needs to find the Snoopy in 3D space — a category it has likely neve...
3dsegmentationopen-vocabulary
Predicting 3D rotations from neural networks sounds straightforward—output a rotation matrix and move on. But rotations live on $\mathrm{SO}(3)$, a curved 3-dimensional manifold...
rotation3ddeep-learning
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
Mathematical notation is the convention that we all use to denote a concept in a concise mathematical formulation, yet sometimes there is more than one way to express the same e...
matrix-calculus
In Machine Learning, supervised problems can be categorized into regression or classification problems. The categorization is quite intuitive as the name indicate. For instance,...
lossf-divergenceregression
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
A Gaussian process is a non-parametric model which can represent a complex function using a growing set of data. Unlike a neural network, which can also learn a complex function...
gaussian-process
In the previous post, we covered variational inference and how to derive update equations. In this post, we will go over a simple Gaussian Mixture Model with the Dirichlet prior...
variational-inferenceexpectation-maximization
Statistical inference involves finding the right model and parameters that represent the distribution of observations well. Let $\mathbf{x}$ be the observations and $\theta$ be ...
expectation-maximizationvariational-inference
Statistical inference often requires modeling the distribution of data. There are two branches of statistical modeling: parametric and non-parametric methods. The former on...
dirichlet-processnonparametric-bayesian
Python layer in Caffe can speed up development process Issue1703 Compile WITH_PYTHON_LAYER option First, you have to build Caffe with WITH_PYTHON_LAYER option 1. Run make clea...
Parametric Regression uses a predefined function form to fit the data best (i.e, we make an assumption about the distribution of data by implicitly modeling them as linear, quad...
gaussian-process-regression
Caffe uses Google Protocol buffer and LMDB or LevelDB to save data in a single unified database file. This allows faster data loading. Saving Database in LMDB I will not cover...
google-protocol-bufferpython
To convert a mesh into a point cloud, one has to sample points that can uniformly cover the surface. To do so, one must choose the number of samples proportional to the area of ...
meshpoint-cloudbarycentric-coordinate
Caffe is one of the most popular open-source neural network frameworks. It is modular, clean, and fast. Extending it is tricky but not as difficult as extending other frameworks...
caffe
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
$ \DeclareMathOperator*{\argmax}{arg\,max} $ Matrix norms and singular values have special relationships. In this post, I’ll summarize a few interesting properties of...
matrix-norm