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unsup_mvs

Learning Unsupervised Multi-View Stereopsis via Robust Photometric Consistency

unsupervised_learning_of_dense_shape_correspondence

unwind

Unwind: Interactive Fish Straightening

up

Official code repository for the paper "Unite the People – Closing the Loop Between 3D and 2D Human Representations".

up-detr

[CVPR2021 Oral] UP-DETR: Unsupervised Pre-training for Object Detection with Transformers

up3date

A Blender add-on to import, edit and export 3D city models encoded in CityJSON v.1.0 format preserving geometries, attributes and semantics

uprightnet

PyTorch implementation of paper "UprightNet: Geometry-Aware Camera Orientation Estimation from Single Images", ICCV 2019

uprightorientation

Upright orientation of 3D shapes with convolutional networks

ups-gcnet

What is Learned in Deep Uncalibrated Photometric Stereo? (ECCV 2020)

urst

Towards Ultra-Resolution Neural Style Transfer via Thumbnail Instance Normalization

usa-buildingheightinference

MSc Graduation Thesis: Height Inference for All USA Building Footprints in the Absence of Height Data

usymlqr

C++ implementation of USYMLQ/USYMQR algorithms for solving unsymmetric linear systems

uwstereonet_disparity

Source code of disparity estimation module for "UWStereoNet: Unsupervised Learning for Depth Estimation and Color Correction of Underwater Stereo Imagery"

value-iteration-networks-pytorch

PyTorch implementation of the Value Iteration Networks (VIN) (NIPS '16 best paper)

vapoursynth

A video processing framework with simplicity in mind

var-aware-mis-pbrt

Implementation of the paper "Variance-Aware Multiple Importance Sampling" for bidirectional path tracing in PBRT.

variable-width-contouring

Code for the computing beads in a slice with varying width for minimizing underfill

variance-aware-path-guiding

Implementation of the paper "Variance-Aware Path Guiding" (SIGGRAPH 2020)

variational-shape-approximation-december-2020---inf574-project-at-ecole-polytechnique

This is a team project implementing a scientific paper called "Variational Shape Approximation" by David Cohen-Steiner, Pierre Alliez and Mathieu Desbrun, published in ACM SIGGRAPH 2004.

variational-surface-cutting

Codebase for "Variational Surface Cutting" by Sharp & Crane, SIGGRAPH 2018

varitex

VariTex: Variational Neural Face Textures, ICCV 2021.

vcd

Code for the paper Learning Visible Connectivity Dynamics for Cloth Smoothing

vcla_vrinteract

Code for SIGGRAPH Asia 2016 Workshop Oral Paper: A Virtual Reality Platform for Dynamic Human-Scene Interaction

vcmeshconv

Learning latent representations of registered meshes is useful for many 3D tasks. Techniques have recently shifted to neural mesh autoencoders. Although they demonstrate higher precision than traditional methods, they remain unable to capture fine-grained deformations. Furthermore, these methods can only be applied to a template-specific surface mesh, and is not applicable to more general meshes, like tetrahedrons and non-manifold meshes. While more general graph convolution methods can be employed, they lack performance in reconstruction precision and require higher memory usage. In this paper, we propose a non-template-specific fully convolutional mesh autoencoder for arbitrary registered mesh data. It is enabled by our novel convolution and (un)pooling operators learned with globally shared weights and locally varying coefficients which can efficiently capture the spatially varying contents presented by irregular mesh connections. Our model outperforms state-of-the-art methods on reconstruction accuracy. In addition, the latent codes of our network are fully localized thanks to the fully convolutional structure, and thus have much higher interpolation capability than many traditional 3D mesh generation models.

vctree-scene-graph-generation

Code for the Scene Graph Generation part of CVPR 2019 oral paper: "Learning to Compose Dynamic Tree Structures for Visual Contexts"

vctree-visual-question-answering

Code for the Visual Question Answering (VQA) part of CVPR 2019 oral paper: "Learning to Compose Dynamic Tree Structures for Visual Contexts"

vdp

Implementation of "Voting for Distortion Points in Geometric Processing"

vdvae

Repository for the paper "Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images"

vectorial-lifting

Lifting Vectorial Variational Problems: A Natural Formulation based on Discrete Exterior Calculus and Geometric Measure Theory, CVPR 2019

vectornet

Semantic Segmentation for Line Drawing Vectorization Using Neural Networks