activity
20162024
most citedOne Noise to Rule Them All: Learning a Unified Model of Spatially-Varying Noise Patterns

3 citations · 6 across the 8 of their papers we have counts for

collaborators

8 papers

cs.GR20243 cited

One Noise to Rule Them All: Learning a Unified Model of Spatially-Varying Noise Patterns

Arman Maesumi, Dylan Hu, Krishi Saripalli +4

Procedural noise is a fundamental component of computer graphics pipelines, offering a flexible way to generate textures that exhibit "natural" random variation. Many different typ…

cs.CV20241 cited

LAESI: Leaf Area Estimation with Synthetic Imagery

Jacek Kałużny, Yannik Schreckenberg, Karol Cyganik +7

We introduce LAESI, a Synthetic Leaf Dataset of 100,000 synthetic leaf images on millimeter paper, each with semantic masks and surface area labels. This dataset provides a resourc…

cs.CV20241 cited

Generating Diverse Agricultural Data for Vision-Based Farming Applications

Mikolaj Cieslak, Umabharathi Govindarajan, Alejandro Garcia +7

We present a specialized procedural model for generating synthetic agricultural scenes, focusing on soybean crops, along with various weeds. This model is capable of simulating dis…

cs.LG2024

A Lennard-Jones Layer for Distribution Normalization

Mulun Na, Jonathan Klein, Biao Zhang +3

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…

cs.RO20241 cited

Gazebo Plants: Simulating Plant-Robot Interaction with Cosserat Rods

Junchen Deng, Samhita Marri, Jonathan Klein +4

Robotic harvesting has the potential to positively impact agricultural productivity, reduce costs, improve food quality, enhance sustainability, and to address labor shortage. In t…

cs.CV2023

3DMiner: Discovering Shapes from Large-Scale Unannotated Image Datasets

Ta-Ying Cheng, Matheus Gadelha, Soren Pirk +4

We present 3DMiner -- a pipeline for mining 3D shapes from challenging large-scale unannotated image datasets. Unlike other unsupervised 3D reconstruction methods, we assume that,…