Search Results for author:
Found 8 papers, 2 papers with code
Sat2Scene: 3D Urban Scene Generation from Satellite Images with Diffusion
Directly generating scenes from satellite imagery offers exciting possibilities for integration into applications like games and map services.
CompNVS: Novel View Synthesis with Scene Completion
We introduce a scalable framework for novel view synthesis from RGB-D images with largely incomplete scene coverage.
Factorized and Controllable Neural Re-Rendering of Outdoor Scene for Photo Extrapolation
Expanding an existing tourist photo from a partially captured scene to a full scene is one of the desired experiences for photography applications.
NVS-MonoDepth: Improving Monocular Depth Prediction with Novel View Synthesis
Building upon the recent progress in novel view synthesis, we propose its application to improve monocular depth estimation.
Ranked #27 on
Monocular Depth Estimation
on KITTI Eigen split
Spatio-Temporal Perturbations for Video Attribution
The attribution method provides a direction for interpreting opaque neural networks in a visual way by identifying and visualizing the input regions/pixels that dominate the output of a network.
Sat2Vid: Street-view Panoramic Video Synthesis from a Single Satellite Image
For geometrical and temporal consistency, our approach explicitly creates a 3D point cloud representation of the scene and maintains dense 3D-2D correspondences across frames that reflect the geometric scene configuration inferred from the satellite view.
Towards Visually Explaining Video Understanding Networks with Perturbation
''Making black box models explainable'' is a vital problem that accompanies the development of deep learning networks.
Topological Map Extraction from Overhead Images
We propose a new approach, named PolyMapper, to circumvent the conventional pixel-wise segmentation of (aerial) images and predict objects in a vector representation directly.
