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CSG on Neural SDFs - Zoë Marschner

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Manage episode 382850516 series 3300270
Contenu fourni par Itzik Ben-Shabat. Tout le contenu du podcast, y compris les épisodes, les graphiques et les descriptions de podcast, est téléchargé et fourni directement par Itzik Ben-Shabat ou son partenaire de plateforme de podcast. Si vous pensez que quelqu'un utilise votre œuvre protégée sans votre autorisation, vous pouvez suivre le processus décrit ici https://fr.player.fm/legal.

Welcome to another exciting episode of the Talking Papers Podcast! In this installment, I had the pleasure of hosting the brilliant Zoë Marschner as we delved into the fascinating world of Constructive Solid Geometry on Neural Signed Distance Fields. This exceptional research paper, published in SIGGRAPH Asia 2023, explores the cutting-edge potential of neural networks in shaping geometric representations.
In our conversation, Zoë enlightened us on the challenges surrounding the editing of shapes encoded by neural Signed Distance Fields (SDFs). While common geometric operators seem like a promising solution, they often result in incorrect outputs known as Pseudo-SDFs, rendering them unusable for downstream tasks. However, fear not! Zoë and her team have galvanized this field with groundbreaking insights.
They characterize the space of Pseudo-SDFs and proffer a novel regularizer called the closest point loss. This ingenious technique encourages the output to be an exact SDF, ensuring accurate shape representation. Their findings have profound implications for operations like CSG (Constructive Solid Geometry) and swept volumes, revolutionizing their applications in fields such as computer-aided design (CAD).
As a former mechanical engineer, I find the concept of combining CSGs with Neural Signed Distance fields to be immensely empowering. The potential for creating intricate and precise designs is mind-boggling!
On a personal note, I couldn't be more thrilled about this episode. Not only were two of the co-authors, Derek and Silvia, previous guests on the podcast, but I also had the pleasure of virtually meeting Zoë for the first time. Recording this episode with her was an absolute blast, and I must say, her enthusiasm and expertise shine through, despite being in the early stages of her career. It's worth mentioning that she has even collaborated with some of the most senior figures in the field!
Join us on this captivating journey into the world of Neural Signed Distance Fields. Don't forget to subscribe and leave your thoughts in the comments section below. We would love to hear your take on this groundbreaking research!
All links and resources are available in the blogpost: https://www.itzikbs.com/CSG_on_NSDF
#TalkingPapersPodcast #SIGGRAPHAsia2023 #SDFs #CSG #shapeediting #neuralnetworks #CAD #research

🎧Subscribe on your favourite podcast app: https://talking.papers.podcast.itzikbs.com

📧Subscribe to our mailing list: http://eepurl.com/hRznqb

🐦Follow us on Twitter: https://twitter.com/talking_papers

🎥YouTube Channel: https://bit.ly/3eQOgwP

  continue reading

33 episodes

Artwork
iconPartager
 
Manage episode 382850516 series 3300270
Contenu fourni par Itzik Ben-Shabat. Tout le contenu du podcast, y compris les épisodes, les graphiques et les descriptions de podcast, est téléchargé et fourni directement par Itzik Ben-Shabat ou son partenaire de plateforme de podcast. Si vous pensez que quelqu'un utilise votre œuvre protégée sans votre autorisation, vous pouvez suivre le processus décrit ici https://fr.player.fm/legal.

Welcome to another exciting episode of the Talking Papers Podcast! In this installment, I had the pleasure of hosting the brilliant Zoë Marschner as we delved into the fascinating world of Constructive Solid Geometry on Neural Signed Distance Fields. This exceptional research paper, published in SIGGRAPH Asia 2023, explores the cutting-edge potential of neural networks in shaping geometric representations.
In our conversation, Zoë enlightened us on the challenges surrounding the editing of shapes encoded by neural Signed Distance Fields (SDFs). While common geometric operators seem like a promising solution, they often result in incorrect outputs known as Pseudo-SDFs, rendering them unusable for downstream tasks. However, fear not! Zoë and her team have galvanized this field with groundbreaking insights.
They characterize the space of Pseudo-SDFs and proffer a novel regularizer called the closest point loss. This ingenious technique encourages the output to be an exact SDF, ensuring accurate shape representation. Their findings have profound implications for operations like CSG (Constructive Solid Geometry) and swept volumes, revolutionizing their applications in fields such as computer-aided design (CAD).
As a former mechanical engineer, I find the concept of combining CSGs with Neural Signed Distance fields to be immensely empowering. The potential for creating intricate and precise designs is mind-boggling!
On a personal note, I couldn't be more thrilled about this episode. Not only were two of the co-authors, Derek and Silvia, previous guests on the podcast, but I also had the pleasure of virtually meeting Zoë for the first time. Recording this episode with her was an absolute blast, and I must say, her enthusiasm and expertise shine through, despite being in the early stages of her career. It's worth mentioning that she has even collaborated with some of the most senior figures in the field!
Join us on this captivating journey into the world of Neural Signed Distance Fields. Don't forget to subscribe and leave your thoughts in the comments section below. We would love to hear your take on this groundbreaking research!
All links and resources are available in the blogpost: https://www.itzikbs.com/CSG_on_NSDF
#TalkingPapersPodcast #SIGGRAPHAsia2023 #SDFs #CSG #shapeediting #neuralnetworks #CAD #research

🎧Subscribe on your favourite podcast app: https://talking.papers.podcast.itzikbs.com

📧Subscribe to our mailing list: http://eepurl.com/hRznqb

🐦Follow us on Twitter: https://twitter.com/talking_papers

🎥YouTube Channel: https://bit.ly/3eQOgwP

  continue reading

33 episodes

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