Search Results for author:
Found 10 papers, 2 papers with code
Deep Fusion Transformer Network with Weighted Vector-Wise Keypoints Voting for Robust 6D Object Pose Estimation
One critical challenge in 6D object pose estimation from a single RGBD image is efficient integration of two different modalities, i. e., color and depth.
Dynamic Clustering Transformer Network for Point Cloud Segmentation
Specifically, we propose novel semantic feature-based dynamic sampling and clustering methods in the encoder, which enables the model to be aware of local semantic homogeneity for local feature aggregation.
NeRF: Neural Radiance Field in 3D Vision, A Comprehensive Review
Neural Radiance Field (NeRF) has recently become a significant development in the field of Computer Vision, allowing for implicit, neural network-based scene representation and novel view synthesis.
3DGTN: 3D Dual-Attention GLocal Transformer Network for Point Cloud Classification and Segmentation
This paper presents a novel point cloud representational learning network, called 3D Dual Self-attention Global Local (GLocal) Transformer Network (3DGTN), for improved feature learning in both classification and segmentation tasks, with the following key contributions.
Transformers in 3D Point Clouds: A Survey
To demonstrate the superiority of Transformers in point cloud analysis, we present comprehensive comparisons of various Transformer-based methods for classification, segmentation, and object detection.
3DCTN: 3D Convolution-Transformer Network for Point Cloud Classification
This paper presents a novel hierarchical framework that incorporates convolution with Transformer for point cloud classification, named 3D Convolution-Transformer Network (3DCTN), to combine the strong and efficient local feature learning ability of convolution with the remarkable global context modeling capability of Transformer.
Sentinel-1 Additive Noise Removal from Cross-Polarization Extra-Wide TOPSAR with Dynamic Least-Squares
We consider a linear denoising model that re-scales the noise field for each subswath, whose parameters are found from a least-squares solution over the objective function.
The impact of data volume on performance of deep learning based building rooftop extraction using very high spatial resolution aerial images
Building rooftop data are of importance in several urban applications and in natural disaster management.
DML-GANR: Deep Metric Learning With Generative Adversarial Network Regularization for High Spatial Resolution Remote Sensing Image Retrieval
With a small number of labeled samples for training, it can save considerable manpower and material resources, especially when the amount of high spatial resolution remote sensing images (HSR-RSIs) increases considerably.
Quantization in Relative Gradient Angle Domain For Building Polygon Estimation
In this paper, we leverage the performance of CNNs, and propose a module that uses prior knowledge of building corners to create angular and concise building polygons from CNN segmentation outputs.
