Search Results for author:
Found 9 papers, 5 papers with code
ASpanFormer: Detector-Free Image Matching with Adaptive Span Transformer
Generating robust and reliable correspondences across images is a fundamental task for a diversity of applications.
Learning Discriminative Feature with CRF for Unsupervised Video Object Segmentation
In this paper, we introduce a novel network, called discriminative feature network (DFNet), to address the unsupervised video object segmentation task.
Ranked #1 on
Video Object Segmentation
on FBMS
Stochastic Bundle Adjustment for Efficient and Scalable 3D Reconstruction
In this work, we propose a stochastic bundle adjustment algorithm which seeks to decompose the RCS approximately inside the LM iterations to improve the efficiency and scalability.
Self-Supervised Monocular 3D Face Reconstruction by Occlusion-Aware Multi-view Geometry Consistency
Recent learning-based approaches, in which models are trained by single-view images have shown promising results for monocular 3D face reconstruction, but they suffer from the ill-posed face pose and depth ambiguity issue.
Ranked #6 on
3D Face Reconstruction
on REALY (side-view)
JSENet: Joint Semantic Segmentation and Edge Detection Network for 3D Point Clouds
Semantic segmentation and semantic edge detection can be seen as two dual problems with close relationships in computer vision.
Ranked #31 on
Semantic Segmentation
on S3DIS
Joint Semantic Segmentation and Boundary Detection using Iterative Pyramid Contexts
In this paper, we present a joint multi-task learning framework for semantic segmentation and boundary detection.
KFNet: Learning Temporal Camera Relocalization using Kalman Filtering
Temporal camera relocalization estimates the pose with respect to each video frame in sequence, as opposed to one-shot relocalization which focuses on a still image.
Learning Fully Dense Neural Networks for Image Semantic Segmentation
On the other hand, it learns more efficiently with the more efficient gradient backpropagation.
Ranked #73 on
Semantic Segmentation
on NYU Depth v2
Learning and Matching Multi-View Descriptors for Registration of Point Clouds
Critical to the registration of point clouds is the establishment of a set of accurate correspondences between points in 3D space.
