Search Results for author:
Found 9 papers, 3 papers with code
DOCTR: Disentangled Object-Centric Transformer for Point Scene Understanding
In this work, we propose a novel Disentangled Object-Centric TRansformer (DOCTR) that explores object-centric representation to facilitate learning with multiple objects for the multiple sub-tasks in a unified manner.
DOR3D-Net: Dense Ordinal Regression Network for 3D Hand Pose Estimation
Recently, dense regression methods have attracted increasing attention in 3D hand pose estimation task, which provide a low computational burden and high accuracy regression way by densely regressing hand joint offset maps.
DVI-SLAM: A Dual Visual Inertial SLAM Network
Recent deep learning based visual simultaneous localization and mapping (SLAM) methods have made significant progress.
NN-Copula-CD: A Copula-Guided Interpretable Neural Network for Change Detection in Heterogeneous Remote Sensing Images
Change detection (CD) in heterogeneous remote sensing images is a practical and challenging issue for real-life emergencies.
MCTNet: A Multi-Scale CNN-Transformer Network for Change Detection in Optical Remote Sensing Images
For the task of change detection (CD) in remote sensing images, deep convolution neural networks (CNNs)-based methods have recently aggregated transformer modules to improve the capability of global feature extraction.
Adversarial samples for deep monocular 6D object pose estimation
We also introduce a new U6DA-Linemod dataset for robustness study of the 6D pose estimation task.
3D Adversarial Attacks Beyond Point Cloud
In order to take advantage of the most effective gradient-based attack, a differentiable sample module that back-propagate the gradient of point cloud to mesh is introduced.
UASNet: Uncertainty Adaptive Sampling Network for Deep Stereo Matching
Extensive experiments show that the proposed method achieves the highest ground truth covering ratio compared with other cascade cost volume based stereo matching methods.
Principal Observation Ray Calibration for Tiled-Lens-Array Integral Imaging Display
Based on this, 3D image artifacts are shown to be effectively removed in a test TLA-IID with challenging misalignments.
