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Found 13 papers, 7 papers with code
Improving Deep Learning-based Defect Detection on Window Frames with Image Processing Strategies
The paper proposes a new defect detection pipeline called InspectNet (IPT-enhanced UNET) that includes the best combination of image enhancement and augmentation techniques for pre-processing the dataset and a Unet model tuned for window frame defect detection and segmentation.
A vision-based autonomous UAV inspection framework for unknown tunnel construction sites with dynamic obstacles
Besides, our framework contains a novel dynamic map module that can simultaneously track dynamic obstacles and represent static obstacles based on an RGB-D camera.
Component Segmentation of Engineering Drawings Using Graph Convolutional Networks
We test our framework in the context of semantic segmentation of text, dimension and, contour components in engineering drawings.
A real-time dynamic obstacle tracking and mapping system for UAV navigation and collision avoidance with an RGB-D camera
Although the popular voxel-based mapping methods can efficiently represent 3D obstacles with arbitrarily complex shapes, they can hardly distinguish between static and dynamic obstacles, leading to the limited performance of obstacle avoidance.
Vision-aided UAV navigation and dynamic obstacle avoidance using gradient-based B-spline trajectory optimization
Although they have shown success in static environments, due to the limitation of map representation, those methods cannot reliably handle static and dynamic obstacles simultaneously.
DPMPC-Planner: A real-time UAV trajectory planning framework for complex static environments with dynamic obstacles
Although plenty of recent works achieve safe navigation in complex static environments with sophisticated mapping algorithms, such as occupancy map and ESDF map, these methods cannot reliably handle dynamic environments due to the mapping limitation from moving obstacles.
Airfoil GAN: Encoding and Synthesizing Airfoils for Aerodynamic Shape Optimization
Our model can (1) encode the existing airfoil into a latent vector and reconstruct the airfoil from that, (2) generate novel airfoils by randomly sampling the latent vectors and mapping the vectors to the airfoil coordinate domain, and (3) synthesize airfoils with desired aerodynamic properties by optimizing learned features via a genetic algorithm.
MuGNet: Multi-Resolution Graph Neural Network for Large-Scale Pointcloud Segmentation
We present MuGNet, a memory-efficient, end-to-end graph neural network framework to perform semantic segmentation on large-scale pointclouds.
Ranked #26 on
Semantic Segmentation
on S3DIS
Autonomous UAV Exploration of Dynamic Environments via Incremental Sampling and Probabilistic Roadmap
Autonomous exploration requires robots to generate informative trajectories iteratively.
Multi-Resolution Graph Neural Network for Large-Scale Pointcloud Segmentation
We present MuGNet, a memory-efficient, end-to-end graph neural network framework to perform semantic segmentation on large-scale pointclouds.
A Deep Reinforcement Learning Approach for Global Routing
At the heart of the proposed method is deep reinforcement learning that enables an agent to produce an optimal policy for routing based on the variety of problems it is presented with leveraging the conjoint optimization mechanism of deep reinforcement learning.
3D Shape Synthesis for Conceptual Design and Optimization Using Variational Autoencoders
We propose a data-driven 3D shape design method that can learn a generative model from a corpus of existing designs, and use this model to produce a wide range of new designs.
Data-driven Upsampling of Point Clouds
High quality upsampling of sparse 3D point clouds is critically useful for a wide range of geometric operations such as reconstruction, rendering, meshing, and analysis.
