Deep Learning Compatible Differentiable X-ray Projections for Inverse Rendering

Compatible Reward Inverse Reinforcement Learning

PROBLEM • Inverse Reinforcement Learning (IRL) problem: recover a reward function explaining a set of expert’s demonstrations. • Advantages of IRL over Behavioral Cloning (BC): – Transferability of the reward. • Issues with some IRL methods: – How to build the features for the reward function? – How to select a reward function among all the optimal ones? – What if no access to the environment? ...

Deep Structure for end-to-end inverse rendering

Inverse rendering in a 3D format denoted to recovering the 3D properties of a scene given 2D input image(s) and is typically done using 3D Morphable Model (3DMM) based methods from single view images. These models formulate each face as a weighted combination of some basis vectors extracted from the training data. In this paper a deep framework is proposed in which the coefficients and basis ve...

A Differentiable Physics Engine for Deep Learning in Robotics

One of the most important fields in robotics is the optimization of controllers. Currently, robots are often treated as a black box in this optimization process, which is the reason why derivative-free optimization methods such as evolutionary algorithms or reinforcement learning are omnipresent. When gradient-based methods are used, models are kept small or rely on finite difference approximat...

Wavelet Projections for Volume Rendering

We extended Gross’s 3 method of volume wavelet rendering by computing splats via an orthogonal projection operator. The method decomposes the volume data into a wavelet pyramid representation in the spline domain. The splats of the basis functions are approximated on a multiresolution grid. Using least-squares approximation ensures the smallest possible error for a given sampling step size. The...

Using deep learning on X-ray images to detect threats