Search Results for author:
Found 14 papers, 6 papers with code
HotSpot: Screened Poisson Equation for Signed Distance Function Optimization
We propose a method, HotSpot, for optimizing neural signed distance functions, based on a relation between the solution of a screened Poisson equation and the distance function.
WatChat: Explaining perplexing programs by debugging mental models
Often, a good explanation for a program's unexpected behavior is a bug in the programmer's code.
Differentiable Visual Computing for Inverse Problems and Machine Learning
This approach is predicated by neural network differentiability, the requirement that analytic derivatives of a given problem's task metric can be computed with respect to neural network's parameters.
Neural Free-Viewpoint Relighting for Glossy Indirect Illumination
In this paper, we demonstrate a hybrid neural-wavelet PRT solution to high-frequency indirect illumination, including glossy reflection, for relighting with changing view.
Factorized Inverse Path Tracing for Efficient and Accurate Material-Lighting Estimation
Inverse path tracing has recently been applied to joint material and lighting estimation, given geometry and multi-view HDR observations of an indoor scene.
Differentiable Rendering of Neural SDFs through Reparameterization
Our method leverages the distance to surface encoded in an SDF and uses quadrature on sphere tracer points to compute this warping function.
Designing Perceptual Puzzles by Differentiating Probabilistic Programs
We design new visual illusions by finding "adversarial examples" for principled models of human perception -- specifically, for probabilistic models, which treat vision as Bayesian inference.
Adversarial examples within the training distribution: A widespread challenge
This result provides evidence supporting theories attributing adversarial examples to the proximity of data to ground-truth class boundaries, and calls into question other theories which do not account for this more stringent definition of adversarial attacks.
Differentiable Vector Graphics Rasterization for Editing and Learning
We introduce a differentiable rasterizer that bridges the vector graphics and raster image domains, enabling powerful raster-based loss functions, optimization procedures, and machine learning techniques to edit and generate vector content.
DiffTaichi: Differentiable Programming for Physical Simulation
We present DiffTaichi, a new differentiable programming language tailored for building high-performance differentiable physical simulators.
Differentiable Visual Computing
Simulating light transport in the presence of multi-bounce glossy effects and motion in 3D rendering is challenging due to the hard-to-sample high-contribution areas.
Inverse Path Tracing for Joint Material and Lighting Estimation
We introduce Inverse Path Tracing, a novel approach to jointly estimate the material properties of objects and light sources in indoor scenes by using an invertible light transport simulation.
Differentiable Monte Carlo Ray Tracing through Edge Sampling
We introduce a general-purpose differentiable ray tracer, which, to our knowledge, is the first comprehensive solution that is able to compute derivatives of scalar functions over a rendered image with respect to arbitrary scene parameters such as camera pose, scene geometry, materials, and lighting parameters.
