Search Results for author:
Found 20 papers, 6 papers with code
Globally Optimal and Efficient Vanishing Point Estimation in Atlanta World
In Atlanta world, given a set of image lines, we aim to cluster them by the unknown-but-sought VPs whose number is unknown.
Enhancing Vectorized Map Perception with Historical Rasterized Maps
To fully exploit a historical map, we propose two novel modules to enhance BEV features and map element queries.
Six-Point Method for Multi-Camera Systems with Reduced Solution Space
A minimal configuration of six PCs is required for generalized cameras.
Leveraging Enhanced Queries of Point Sets for Vectorized Map Construction
In autonomous driving, the high-definition (HD) map plays a crucial role in localization and planning.
Affine Correspondences between Multi-Camera Systems for Relative Pose Estimation
We present a novel method to compute the relative pose of multi-camera systems using two affine correspondences (ACs).
Transition Propagation Graph Neural Networks for Temporal Networks
The proposed TIP-GNN focuses on the bilevel graph structure in temporal networks: besides the explicit interaction graph, a node's sequential interactions can also be constructed as a transition graph.
Learning To Identify Correct 2D-2D Line Correspondences on Sphere
In contrast, we propose the first approach suitable for both structured and unstructured scenes.
On Relative Pose Recovery for Multi-Camera Systems
This solver generation method is also naturally applied to relative pose estimation from PCs, resulting in a new six-point method for multi-camera systems.
Hybrid Rotation Averaging: A Fast and Robust Rotation Averaging Approach
We push the envelope of rotation averaging by leveraging the advantages of a global RA method and a local RA method.
Minimal Cases for Computing the Generalized Relative Pose using Affine Correspondences
We propose three novel solvers for estimating the relative pose of a multi-camera system from affine correspondences (ACs).
A Certifiably Globally Optimal Solution to Generalized Essential Matrix Estimation
We present a convex optimization approach for generalized essential matrix (GEM) estimation.
Minimal Solutions for Relative Pose with a Single Affine Correspondence
In this paper we present four cases of minimal solutions for two-view relative pose estimation by exploiting the affine transformation between feature points and we demonstrate efficient solvers for these cases.
An Evaluation of Feature Matchers for Fundamental Matrix Estimation
According to this, we propose three high-quality matching systems and a Coarse-to-Fine RANSAC estimator.
An Efficient Solution to Non-Minimal Case Essential Matrix Estimation
In this paper, we propose a certifiably globally optimal and efficient solver for the $N$-point problem.
POI Semantic Model with a Deep Convolutional Structure
An accurate similarity calculation is challenging since the mismatch between a query and a retrieval text may exist in the case of a mistyped query or an alias inquiry.
Unsupervised Learnable Sinogram Inpainting Network (SIN) for Limited Angle CT reconstruction
In this paper, we propose a sinogram inpainting network (SIN) to solve limited-angle CT reconstruction problem, which is a very challenging ill-posed issue and of great interest for several clinical applications.
Medical Physics Image and Video Processing
Density-Based Region Search with Arbitrary Shape for Object Localization
Typically, the region search methods project the score of a classifier into an image plane, and then search the region with the maximal score.
Feature and Region Selection for Visual Learning
There are four main benefits of our approach: (1) Our approach accommodates non-linear additive kernels such as the popular $\chi^2$ and intersection kernel; (2) our approach is able to handle both regions in images and spatio-temporal regions in videos in a unified way; (3) the feature selection problem is convex, and both problems can be solved using a scalable reduced gradient method; (4) we point out strong connections with multiple kernel learning and multiple instance learning approaches.
FastMMD: Ensemble of Circular Discrepancy for Efficient Two-Sample Test
Taking advantage of sampling of Fourier transform, FastMMD decreases the time complexity for MMD calculation from $O(N^2 d)$ to $O(L N d)$, where $N$ and $d$ are the size and dimension of the sample set, respectively.
Robust Estimation of Nonrigid Transformation for Point Set Registration
In the second step, we estimate the transformation using a robust estimator called L 2 E. This is the main novelty of our approach and it enables us to deal with the noise and outliers which arise in the correspondence step.
