Search Results for author:
Found 6 papers, 3 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.
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
ImpliCity: City Modeling from Satellite Images with Deep Implicit Occupancy Fields
To make full use of the point cloud and the underlying images, we introduce ImpliCity, a neural representation of the 3D scene as an implicit, continuous occupancy field, driven by learned embeddings of the point cloud and a stereo pair of ortho-photos.
