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Found 23 papers, 14 papers with code
Marigold: Affordable Adaptation of Diffusion-Based Image Generators for Image Analysis
The success of deep learning in computer vision over the past decade has hinged on large labeled datasets and strong pretrained models.
Marigold-DC: Zero-Shot Monocular Depth Completion with Guided Diffusion
Depth completion upgrades sparse depth measurements into dense depth maps guided by a conventional image.
Video Depth without Video Models
Video depth estimation lifts monocular video clips to 3D by inferring dense depth at every frame.
BetterDepth: Plug-and-Play Diffusion Refiner for Zero-Shot Monocular Depth Estimation
For the training of such a refiner, we propose global pre-alignment and local patch masking methods to ensure BetterDepth remains faithful to the depth conditioning while learning to add fine-grained scene details.
Consistency^2: Consistent and Fast 3D Painting with Latent Consistency Models
Generative 3D Painting is among the top productivity boosters in high-resolution 3D asset management and recycling.
I-Design: Personalized LLM Interior Designer
Interior design allows us to be who we are and live how we want - each design is as unique as our distinct personality.
Point2Building: Reconstructing Buildings from Airborne LiDAR Point Clouds
What makes 3D building reconstruction from airborne LiDAR hard is the large diversity of building designs and especially roof shapes, the low and varying point density across the scene, and the often incomplete coverage of building facades due to occlusions by vegetation or to the viewing angle of the sensor.
Point2CAD: Reverse Engineering CAD Models from 3D Point Clouds
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 designer significant time when iterating on in-the-wild objects.
DGInStyle: Domain-Generalizable Semantic Segmentation with Image Diffusion Models and Stylized Semantic Control
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 condition their output on other modalities, such as semantic maps.
Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation
Monocular depth estimation is a fundamental computer vision task.
Ranked #6 on
Monocular Depth Estimation
on ETH3D
Breathing New Life into 3D Assets with Generative Repainting
Diffusion-based text-to-image models ignited immense attention from the vision community, artists, and content creators.
EDAPS: Enhanced Domain-Adaptive Panoptic Segmentation
EDAPS significantly improves the state-of-the-art performance for panoptic segmentation UDA by a large margin of 20% on SYNTHIA-to-Cityscapes and even 72% on the more challenging SYNTHIA-to-Mapillary Vistas.
Ranked #2 on
Domain Adaptation
on Panoptic SYNTHIA-to-Mapillary
Towards Practical Control of Singular Values of Convolutional Layers
We demonstrate the improved properties of modern CNNs with our method and analyze its impact on the model performance, calibration, and adversarial robustness.
DiffDreamer: Towards Consistent Unsupervised Single-view Scene Extrapolation with Conditional Diffusion Models
Scene extrapolation -- the idea of generating novel views by flying into a given image -- is a promising, yet challenging task.
Ranked #1 on
Perpetual View Generation
on LHQ
TT-NF: Tensor Train Neural Fields
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.
Pix2NeRF: Unsupervised Conditional $π$-GAN for Single Image to Neural Radiance Fields Translation
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.
Pix2NeRF: Unsupervised Conditional p-GAN for Single Image to Neural Radiance Fields Translation
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.
Learning to Relate Depth and Semantics for Unsupervised Domain Adaptation
Specifically, we show that: (1) our approach improves performance on all tasks when they are complementary and mutually dependent; (2) the CTRL helps to improve both semantic segmentation and depth estimation tasks performance in the challenging UDA setting; (3) the proposed ISL training scheme further improves the semantic segmentation performance.
Exploring Relational Context for Multi-Task Dense Prediction
Our goal is to find the most efficient way to refine each task prediction by capturing cross-task contexts dependent on tasks' relations.
Ranked #91 on
Semantic Segmentation
on NYU Depth v2
Spectral Tensor Train Parameterization of Deep Learning Layers
We study low-rank parameterizations of weight matrices with embedded spectral properties in the Deep Learning context.
Reparameterizing Convolutions for Incremental Multi-Task Learning without Task Interference
First, enabling the model to be inherently incremental, continuously incorporating information from new tasks without forgetting the previously learned ones (incremental learning).
T-Basis: a Compact Representation for Neural Networks
Each of the tensors in the set is modeled using Tensor Rings, though the concept applies to other Tensor Networks.
Gated CRF Loss for Weakly Supervised Semantic Image Segmentation
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 expensive to collect, both in terms of time and money.
Image Segmentation
Weakly supervised Semantic Segmentation
+1
