Tips and Tricks for 3D Reconstruction in Different Environments

Computer Vision Decoded

Το περιεχόμενο παρέχεται από το Jonathan Stephens. Όλο το περιεχόμενο podcast, συμπεριλαμβανομένων των επεισοδίων, των γραφικών και των περιγραφών podcast, μεταφορτώνεται και παρέχεται απευθείας από τον Jonathan Stephens ή τον συνεργάτη της πλατφόρμας podcast. Εάν πιστεύετε ότι κάποιος χρησιμοποιεί το έργο σας που προστατεύεται από πνευματικά δικαιώματα χωρίς την άδειά σας, μπορείτε να ακολουθήσετε τη διαδικασία που περιγράφεται εδώ https://el.player.fm/legal.

9M ago 1:21:23

MP3•Αρχική οθόνη επεισοδίου

Fetch error

Hmmm there seems to be a problem fetching this series right now. Last successful fetch was on July 14, 2025 21:32 (5M ago)

What now? This series will be checked again in the next day. If you believe it should be working, please verify the publisher's feed link below is valid and includes actual episode links. You can contact support to request the feed be immediately fetched.

In this episode, we discuss practical tips and challenges in 3D reconstruction from images, focusing on various environments such as urban, indoor, and outdoor settings. We explore issues like repetitive structures, lighting conditions, and the impact of reflections and shadows on reconstruction quality. The conversation also touches on the importance of camera motion, lens distortion, and the role of machine learning in enhancing reconstruction processes. Listeners gain insights into optimizing their 3D capture techniques for better results.

Key Takeaways

Repetitive structures can confuse computer vision algorithms.
Lighting conditions greatly affect image quality and reconstruction accuracy.
Wide-angle lenses can help capture more unique features.
Indoor environments present unique challenges like textureless walls.
Aerial imaging requires careful management of lens distortion.
Understanding the application context is crucial for effective 3D reconstruction.
Camera motion should be varied to avoid distortion and drift.
Planning captures based on goals can lead to better results.

This episode is brought to you by EveryPoint. Learn more about how EveryPoint is building an infinitely scalable data collection and processing platform for the next generation of spatial computing applications and services. Learn more at https://www.everypoint.io

18 επεισόδια

#Science #Tech #Jonathan Stephens #Computer Vision #Machine Learning #Apple #Photogrammetry #Spatial Computing