Nothing Special   »   [go: up one dir, main page]
Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Computer Science > Computer Vision and Pattern Recognition

arXiv:2408.16472 (cs)
[Submitted on 29 Aug 2024]

Title:Creating a Segmented Pointcloud of Grapevines by Combining Multiple Viewpoints Through Visual Odometry

Authors:Michael Adlerstein, Angelo Bratta, João Carlos Virgolino Soares, Giovanni Dessy, Miguel Fernandes, Matteo Gatti, Claudio Semini
View PDF HTML (experimental)
Abstract:Grapevine winter pruning is a labor-intensive and repetitive process that significantly influences the quality and quantity of the grape harvest and produced wine of the following season. It requires a careful and expert detection of the point to be cut. Because of its complexity, repetitive nature and time constraint, the task requires skilled labor that needs to be trained. This extended abstract presents the computer vision pipeline employed in project Vinum, using detectron2 as a segmentation network and keypoint visual odometry to merge different observation into a single pointcloud used to make informed pruning decisions.
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2408.16472 [cs.CV]
  (or arXiv:2408.16472v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2408.16472

Submission history

From: Angelo Bratta [view email]
[v1] Thu, 29 Aug 2024 12:04:03 UTC (5,341 KB)
Full-text links:

Access Paper:

view license
Current browse context:
cs.CV
< prev   |   next >
new | recent | 2024-08
Change to browse by:
cs

References & Citations

export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)