Search Results for author:
Found 8 papers, 4 papers with code
AA-RMVSNet: Adaptive Aggregation Recurrent Multi-view Stereo Network
To overcome the difficulty of varying occlusion in complex scenes, we propose an inter-view cost volume aggregation module for adaptive pixel-wise view aggregation, which is able to preserve better-matched pairs among all views.
Ranked #10 on
Point Clouds
on Tanks and Temples
(Mean F1 (Intermediate) metric)
Deep Learning for Multi-View Stereo via Plane Sweep: A Survey
3D reconstruction has lately attracted increasing attention due to its wide application in many areas, such as autonomous driving, robotics and virtual reality.
Dense Hybrid Recurrent Multi-view Stereo Net with Dynamic Consistency Checking
In this paper, we propose an efficient and effective dense hybrid recurrent multi-view stereo net with dynamic consistency checking, namely $D^{2}$HC-RMVSNet, for accurate dense point cloud reconstruction.
Graph-Based Parallel Large Scale Structure from Motion
After local reconstructions, we construct a minimum spanning tree (MinST) to find accurate similarity transformations.
Bundle Adjustment Revisited
3D reconstruction has been developing all these two decades, from moderate to medium size and to large scale.
Pyramid Multi-view Stereo Net with Self-adaptive View Aggregation
n this paper, we propose an effective and efficient pyramid multi-view stereo (MVS) net with self-adaptive view aggregation for accurate and complete dense point cloud reconstruction.
X-GANs: Image Reconstruction Made Easy for Extreme Cases
Image reconstruction including image restoration and denoising is a challenging problem in the field of image computing.
Foreground segmentation based on multi-resolution and matting
We propose a foreground segmentation algorithm that does foreground extraction under different scales and refines the result by matting.
