Search Results for author:
Found 7 papers, 2 papers with code
XDGAN: Multi-Modal 3D Shape Generation in 2D Space
Specifically, we propose a novel method to convert 3D shapes into compact 1-channel geometry images and leverage StyleGAN3 and image-to-image translation networks to generate 3D objects in 2D space.
Enhancing Photorealism Enhancement
We confirm the benefits of our contributions in controlled experiments and report substantial gains in stability and realism in comparison to recent image-to-image translation methods and a variety of other baselines.
Intrinsic Autoencoders for Joint Neural Rendering and Intrinsic Image Decomposition
Neural rendering techniques promise efficient photo-realistic image synthesis while at the same time providing rich control over scene parameters by learning the physical image formation process.
Geometric Image Synthesis
The task of generating natural images from 3D scenes has been a long standing goal in computer graphics.
Bounding Boxes, Segmentations and Object Coordinates: How Important Is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?
Existing methods for 3D scene flow estimation often fail in the presence of large displacement or local ambiguities, e. g., at texture-less or reflective surfaces.
Augmented Reality Meets Computer Vision : Efficient Data Generation for Urban Driving Scenes
Further, we demonstrate the utility of our approach on training standard deep models for semantic instance segmentation and object detection of cars in outdoor driving scenes.
A Study of Lagrangean Decompositions and Dual Ascent Solvers for Graph Matching
We study the quadratic assignment problem, in computer vision also known as graph matching.
