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Found 11 papers, 1 papers with code
TokenMotion: Motion-Guided Vision Transformer for Video Camouflaged Object Detection Via Learnable Token Selection
The area of Video Camouflaged Object Detection (VCOD) presents unique challenges in the field of computer vision due to texture similarities between target objects and their surroundings, as well as irregular motion patterns caused by both objects and camera movement.
TransUPR: A Transformer-based Uncertain Point Refiner for LiDAR Point Cloud Semantic Segmentation
Uncertain points are sampled from coarse semantic segmentation results of 2D image segmentation where uncertain points are located close to the object boundaries in the 2D range image representation and 3D spherical projection background points.
STPLS3D: A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point Cloud Dataset
Specifically, we introduce a synthetic aerial photogrammetry point clouds generation pipeline that takes full advantage of open geospatial data sources and off-the-shelf commercial packages.
Ground material classification for UAV-based photogrammetric 3D data A 2D-3D Hybrid Approach
For ground material segmentation, we utilized an existing convolutional neural network architecture (i. e., 3DMV) which was originally designed for segmenting RGB-D sensed indoor data.
3D photogrammetry point cloud segmentation using a model ensembling framework
In this paper, we introduce a model ensembling framework for segmenting a 3D photogrammetry point cloud into top-level terrain elements (i. e., ground, human-made objects, and vegetation).
Utilizing Satellite Imagery Datasets and Machine Learning Data Models to Evaluate Infrastructure Change in Undeveloped Regions
A goal of this research is to allow automated monitoring for largescale infrastructure projects, such as railways, to determine reliable metrics that define and predict the direction construction initiatives could take, allowing for a directed monitoring via narrowed and targeted satellite imagery requests.
Semantic Segmentation and Data Fusion of Microsoft Bing 3D Cities and Small UAV-based Photogrammetric Data
This paper discusses the next steps in extending our designed data segmentation framework for segmenting 3D city data.
Generating synthetic photogrammetric data for training deep learning based 3D point cloud segmentation models
At I/ITSEC 2019, the authors presented a fully-automated workflow to segment 3D photogrammetric point-clouds/meshes and extract object information, including individual tree locations and ground materials (Chen et al., 2019).
Fully Automated Photogrammetric Data Segmentation and Object Information Extraction Approach for Creating Simulation Terrain
The results showed that 3D mesh trees could be replaced with geo-typical 3D tree models using the extracted individual tree locations.
Photogrammetric point cloud segmentation and object information extraction for creating virtual environments and simulations
Thus, segmenting generated point clouds and meshes and extracting the associated object information is a necessary step.
Semantic Modeling of Outdoor Scenes for the Creation of Virtual Environments and Simulations
Efforts from both academia and industry have adopted photogrammetric techniques to generate visually compelling 3D models for the creation of virtual environments and simulations.
