Search Results for author:
Found 10 papers, 4 papers with code
TCLC-GS: Tightly Coupled LiDAR-Camera Gaussian Splatting for Surrounding Autonomous Driving Scenes
Most 3D Gaussian Splatting (3D-GS) based methods for urban scenes initialize 3D Gaussians directly with 3D LiDAR points, which not only underutilizes LiDAR data capabilities but also overlooks the potential advantages of fusing LiDAR with camera data.
Feature Shrinkage Pyramid for Camouflaged Object Detection with Transformers
Vision transformers have recently shown strong global context modeling capabilities in camouflaged object detection.
GWRBoost:A geographically weighted gradient boosting method for explainable quantification of spatially-varying relationships
The geographically weighted regression (GWR) is an essential tool for estimating the spatial variation of relationships between dependent and independent variables in geographical contexts.
EPCL: Frozen CLIP Transformer is An Efficient Point Cloud Encoder
These token embeddings are concatenated with a task token and fed into the frozen CLIP transformer to learn point cloud representation.
PolyBuilding: Polygon Transformer for End-to-End Building Extraction
Given a set of polygon queries, the model learns the relations among them and encodes context information from the image to predict the final set of building polygons with fixed vertex numbers.
DouFu: A Double Fusion Joint Learning Method For Driving Trajectory Representation
The attention fusion module incorporates route features with movement features to create a better spatial-temporal embedding.
Scribble-based Boundary-aware Network for Weakly Supervised Salient Object Detection in Remote Sensing Images
To this end, in this paper, we propose a novel weakly-supervised salient object detection framework to predict the saliency of remote sensing images from sparse scribble annotations.
Multi-level Cross-modal Interaction Network for RGB-D Salient Object Detection
Our MCI-Net includes two key components: 1) a cross-modal feature learning network, which is used to learn the high-level features for the RGB images and depth cues, effectively enabling the correlations between the two sources to be exploited; and 2) a multi-level interactive integration network, which integrates multi-level cross-modal features to boost the SOD performance.
Contrast-weighted Dictionary Learning Based Saliency Detection for Remote Sensing Images
Furthermore, by using the proposed joint saliency measure, a variety of saliency maps are generated based on the discriminant dictionary.
Understanding the mesoscopic scaling patterns within cities
Understanding quantitative relationships between urban elements is crucial for a wide range of applications.
Physics and Society
