Search Results for author:
Found 5 papers, 4 papers with code
Learning to Recover 3D Scene Shape from a Single Image
Despite significant progress in monocular depth estimation in the wild, recent state-of-the-art methods cannot be used to recover accurate 3D scene shape due to an unknown depth shift induced by shift-invariant reconstruction losses used in mixed-data depth prediction training, and possible unknown camera focal length.
Ranked #1 on
Indoor Monocular Depth Estimation
on DIODE
(using extra training data)
Towards Accurate Reconstruction of 3D Scene Shape from A Single Monocular Image
To do so, we propose a two-stage framework that first predicts depth up to an unknown scale and shift from a single monocular image, and then exploits 3D point cloud data to predict the depth shift and the camera's focal length that allow us to recover 3D scene shapes.
SSH: A Self-Supervised Framework for Image Harmonization
Image harmonization aims to improve the quality of image compositing by matching the "appearance" (\eg, color tone, brightness and contrast) between foreground and background images.
Towards Domain-agnostic Depth Completion
Our method leverages a data-driven prior in the form of a single image depth prediction network trained on large-scale datasets, the output of which is used as an input to our model.
Layered Depth Refinement with Mask Guidance
Depth maps are used in a wide range of applications from 3D rendering to 2D image effects such as Bokeh.
