Search Results for author:
Found 8 papers, 2 papers with code
NeRF-In: Free-Form NeRF Inpainting with RGB-D Priors
In this paper, we introduce the first framework that enables users to remove unwanted objects or retouch undesired regions in a 3D scene represented by a pre-trained NeRF without any category-specific data and training.
StylePart: Image-based Shape Part Manipulation
The attribute codes of the manipulated 3D shape are then "backwardly mapped" to the image latent code to obtain the final manipulated image.
FishNet: A Camera Localizer using Deep Recurrent Networks
This paper proposes a robust localization system that employs deep learning for better scene representation, and enhances the accuracy of 6-DOF camera pose estimation.
Transferring Deep Reinforcement Learning with Adversarial Objective and Augmentation
In the past few years, deep reinforcement learning has been proven to solve problems which have complex states like video games or board games.
Quantitative Analysis of Automatic Image Cropping Algorithms: A Dataset and Comparative Study
Automatic photo cropping is an important tool for improving visual quality of digital photos without resorting to tedious manual selection.
Integrating Dashcam Views Through Inter-Video Mapping
In this paper, an inter-video mapping approach is proposed to integrate video footages from two dashcams installed on a preceding and its following vehicle to provide the illusion that the driver of the following vehicle can see-through the preceding one.
Descriptor Ensemble: An Unsupervised Approach to Descriptor Fusion in the Homography Space
Inspired by the observation that the homographies of correct feature correspondences vary smoothly along the spatial domain, our approach stands on the unsupervised nature of feature matching, and can select a good descriptor for matching each feature point.
Robust Feature Matching with Alternate Hough and Inverted Hough Transforms
Inspired by the fact that nearby features on the same object share coherent homographies in matching, we cast the task of feature matching as a density estimation problem in the Hough space spanned by the hypotheses of homographies.
