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Found 13 papers, 1 papers with code
LAESI: Leaf Area Estimation with Synthetic Imagery
We introduce LAESI, a Synthetic Leaf Dataset of 100, 000 synthetic leaf images on millimeter paper, each with semantic masks and surface area labels.
Generating Diverse Agricultural Data for Vision-Based Farming Applications
The integration of real-world textures and environmental factors into the procedural generation process enhances the photorealism and applicability of the synthetic data.
A Lennard-Jones Layer for Distribution Normalization
We introduce the Lennard-Jones layer (LJL) for the equalization of the density of 2D and 3D point clouds through systematically rearranging points without destroying their overall structure (distribution normalization).
Gazebo Plants: Simulating Plant-Robot Interaction with Cosserat Rods
Robotic harvesting has the potential to positively impact agricultural productivity, reduce costs, improve food quality, enhance sustainability, and to address labor shortage.
Deep Aramaic: Towards a Synthetic Data Paradigm Enabling Machine Learning in Epigraphy
Our results validate the model's capabilities in handling diverse real-world scenarios, proving the viability of our synthetic data approach and avoiding the dependence on scarce training data that has constrained epigraphic analysis.
Zero-Level-Set Encoder for Neural Distance Fields
Overall, this can help reduce the computational overhead of training and evaluating neural distance fields, as well as enabling the application to difficult shapes.
RLSS: A Deep Reinforcement Learning Algorithm for Sequential Scene Generation
We present RLSS: a reinforcement learning algorithm for sequential scene generation.
Synthetic 3D Data Generation Pipeline for Geometric Deep Learning in Architecture
The variety of annotations, the flexibility to customize the generated building and dataset parameters make this framework suitable for multiple deep learning tasks, including geometric deep learning that requires direct 3D supervision.
Domain Adaptation with Morphologic Segmentation
We present a novel domain adaptation framework that uses morphologic segmentation to translate images from arbitrary input domains (real and synthetic) into a uniform output domain.
Accurately Solving Physical Systems with Graph Learning
In this contribution, we introduce a novel method to accelerate iterative solvers for physical systems with graph networks (GNs) by predicting the initial guesses to reduce the number of iterations.
Learning a Controller Fusion Network by Online Trajectory Filtering for Vision-based UAV Racing
A common approach is to learn an end-to-end policy that directly predicts controls from raw images by imitating an expert.
OIL: Observational Imitation Learning
Recent work has explored the problem of autonomous navigation by imitating a teacher and learning an end-to-end policy, which directly predicts controls from raw images.
Teaching UAVs to Race: End-to-End Regression of Agile Controls in Simulation
Automating the navigation of unmanned aerial vehicles (UAVs) in diverse scenarios has gained much attention in recent years.
