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Found 11 papers, 6 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.
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.
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
Neural Fields with Thermal Activations for Arbitrary-Scale Super-Resolution
We present a novel way to design neural fields such that points can be queried with an adaptive Gaussian PSF, so as to guarantee correct anti-aliasing at any desired output resolution.
High-resolution Population Maps Derived from Sentinel-1 and Sentinel-2
With our work we aim to democratize access to up-to-date and high-resolution population maps, recognizing that some regions faced with particularly strong population dynamics may lack the resources for costly micro-census campaigns.
Guided Depth Super-Resolution by Deep Anisotropic Diffusion
In this work, we propose a novel approach which combines guided anisotropic diffusion with a deep convolutional network and advances the state of the art for guided depth super-resolution.
Fine-grained Population Mapping from Coarse Census Counts and Open Geodata
Fine-grained population maps are needed in several domains, like urban planning, environmental monitoring, public health, and humanitarian operations.
Urban Change Forecasting from Satellite Images
In Stage 1, a U-Net backbone is pretrained within a Siamese network architecture that aims to solve a (building) change detection task.
DSM Refinement with Deep Encoder-Decoder Networks
However, the calculated DSMs suffer from noise, artefacts, and data holes that have to be manually cleaned up in a time-consuming process.
Crop Classification under Varying Cloud Cover with Neural Ordinary Differential Equations
We propose to use neural ordinary differential equations (NODEs) in combination with RNNs to classify crop types in irregularly spaced image sequences.
