54 citations · 123 across the 19 of their papers we have counts for
22 papers
Marigold: Affordable Adaptation of Diffusion-Based Image Generators for Image Analysis
Bingxin Ke, Kevin Qu, Tianfu Wang +5
The success of deep learning in computer vision over the past decade has hinged on large labeled datasets and strong pretrained models. In data-scarce settings, the quality of thes…
Marigold-DC: Zero-Shot Monocular Depth Completion with Guided Diffusion
Massimiliano Viola, Kevin Qu, Nando Metzger +4
Depth completion upgrades sparse depth measurements into dense depth maps guided by a conventional image. Existing methods for this highly ill-posed task operate in tightly constra…
Video Depth without Video Models
Bingxin Ke, Dominik Narnhofer, Shengyu Huang +5
Video depth estimation lifts monocular video clips to 3D by inferring dense depth at every frame. Recent advances in single-image depth estimation, brought about by the rise of lar…
BetterDepth: Plug-and-Play Diffusion Refiner for Zero-Shot Monocular Depth Estimation
Xiang Zhang, Bingxin Ke, Hayko Riemenschneider +5
By training over large-scale datasets, zero-shot monocular depth estimation (MDE) methods show robust performance in the wild but often suffer from insufficient detail. Although re…
Consistency^2: Consistent and Fast 3D Painting with Latent Consistency Models
Tianfu Wang, Anton Obukhov, Konrad Schindler
Generative 3D Painting is among the top productivity boosters in high-resolution 3D asset management and recycling. Ever since text-to-image models became accessible for inference…
I-Design: Personalized LLM Interior Designer
Ata Çelen, Guo Han, Konrad Schindler +4
Interior design allows us to be who we are and live how we want - each design is as unique as our distinct personality. However, it is not trivial for non-professionals to express…
Point2Building: Reconstructing Buildings from Airborne LiDAR Point Clouds
Yujia Liu, Anton Obukhov, Jan Dirk Wegner +1
We present a learning-based approach to reconstruct buildings as 3D polygonal meshes from airborne LiDAR point clouds. What makes 3D building reconstruction from airborne LiDAR har…
Point2CAD: Reverse Engineering CAD Models from 3D Point Clouds
Yujia Liu, Anton Obukhov, Jan Dirk Wegner +1
Computer-Aided Design (CAD) model reconstruction from point clouds is an important problem at the intersection of computer vision, graphics, and machine learning; it saves the desi…
DGInStyle: Domain-Generalizable Semantic Segmentation with Image Diffusion Models and Stylized Semantic Control
Yuru Jia, Lukas Hoyer, Shengyu Huang +4
Large, pretrained latent diffusion models (LDMs) have demonstrated an extraordinary ability to generate creative content, specialize to user data through few-shot fine-tuning, and…
Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation
Bingxin Ke, Anton Obukhov, Shengyu Huang +3
Monocular depth estimation is a fundamental computer vision task. Recovering 3D depth from a single image is geometrically ill-posed and requires scene understanding, so it is not…
Breathing New Life into 3D Assets with Generative Repainting
Tianfu Wang, Menelaos Kanakis, Konrad Schindler +2
Diffusion-based text-to-image models ignited immense attention from the vision community, artists, and content creators. Broad adoption of these models is due to significant improv…
EDAPS: Enhanced Domain-Adaptive Panoptic Segmentation
Suman Saha, Lukas Hoyer, Anton Obukhov +2
With autonomous industries on the rise, domain adaptation of the visual perception stack is an important research direction due to the cost savings promise. Much prior art was dedi…
Towards Practical Control of Singular Values of Convolutional Layers
Alexandra Senderovich, Ekaterina Bulatova, Anton Obukhov +1
In general, convolutional neural networks (CNNs) are easy to train, but their essential properties, such as generalization error and adversarial robustness, are hard to control. Re…
DiffDreamer: Towards Consistent Unsupervised Single-view Scene Extrapolation with Conditional Diffusion Models
Shengqu Cai, Eric Ryan Chan, Songyou Peng +4
Scene extrapolation -- the idea of generating novel views by flying into a given image -- is a promising, yet challenging task. For each predicted frame, a joint inpainting and 3D…
TT-NF: Tensor Train Neural Fields
Anton Obukhov, Mikhail Usvyatsov, Christos Sakaridis +2
Learning neural fields has been an active topic in deep learning research, focusing, among other issues, on finding more compact and easy-to-fit representations. In this paper, we…
Pix2NeRF: Unsupervised Conditional -GAN for Single Image to Neural Radiance Fields Translation
Shengqu Cai, Anton Obukhov, Dengxin Dai +1
We propose a pipeline to generate Neural Radiance Fields~(NeRF) of an object or a scene of a specific class, conditioned on a single input image. This is a challenging task, as tra…
Learning to Relate Depth and Semantics for Unsupervised Domain Adaptation
Suman Saha, Anton Obukhov, Danda Pani Paudel +4
We present an approach for encoding visual task relationships to improve model performance in an Unsupervised Domain Adaptation (UDA) setting. Semantic segmentation and monocular d…
Exploring Relational Context for Multi-Task Dense Prediction
David Bruggemann, Menelaos Kanakis, Anton Obukhov +2
The timeline of computer vision research is marked with advances in learning and utilizing efficient contextual representations. Most of them, however, are targeted at improving mo…
Spectral Tensor Train Parameterization of Deep Learning Layers
Anton Obukhov, Maxim Rakhuba, Alexander Liniger +4
We study low-rank parameterizations of weight matrices with embedded spectral properties in the Deep Learning context. The low-rank property leads to parameter efficiency and permi…
Reparameterizing Convolutions for Incremental Multi-Task Learning without Task Interference
Menelaos Kanakis, David Bruggemann, Suman Saha +3
Multi-task networks are commonly utilized to alleviate the need for a large number of highly specialized single-task networks. However, two common challenges in developing multi-ta…
T-Basis: a Compact Representation for Neural Networks
Anton Obukhov, Maxim Rakhuba, Stamatios Georgoulis +3
We introduce T-Basis, a novel concept for a compact representation of a set of tensors, each of an arbitrary shape, which is often seen in Neural Networks. Each of the tensors in t…
Gated CRF Loss for Weakly Supervised Semantic Image Segmentation
Anton Obukhov, Stamatios Georgoulis, Dengxin Dai +1
State-of-the-art approaches for semantic segmentation rely on deep convolutional neural networks trained on fully annotated datasets, that have been shown to be notoriously expensi…