Differentiable 3D Scene Representations With Point-Based Neural Methods
I tiakina i:
| I whakaputaina i: | ProQuest Dissertations and Theses (2025) |
|---|---|
| Kaituhi matua: | |
| I whakaputaina: |
ProQuest Dissertations & Theses
|
| Ngā marau: | |
| Urunga tuihono: | Citation/Abstract Full Text - PDF |
| Ngā Tūtohu: |
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3275489203 | ||
| 003 | UK-CbPIL | ||
| 020 | |a 9798263351267 | ||
| 035 | |a 3275489203 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 66569 |2 nlm | ||
| 100 | 1 | |a Börcsök, Barnabás Barney | |
| 245 | 1 | |a Differentiable 3D Scene Representations With Point-Based Neural Methods | |
| 260 | |b ProQuest Dissertations & Theses |c 2025 | ||
| 513 | |a Dissertation/Thesis | ||
| 520 | 3 | |a This thesis explores reconstructing explicit scene geometry via geometry particles carrying local Lagrangian patches. We formulate a signed distance field as a weighted sum of moving basis functions and describe an optimization framework to fit target shapes in both 2D and 3D. Experiments on canonical geometry meshes show that with a modest number of particles, our approach can capture coarse geometric structures while providing intuitive control and interpretable local geometry images in a storage-efficient representation. Although these preliminary results do not yet match state-of-the-art accuracy, they highlight the promise of a particle-based, differentiable explicit representation that is suitable to inspire further work in a vast array of workflow improvements from digital sculpting to generative modeling. We conclude by discussing avenues for further research on improving particle placement, blending strategies, and interactive editing capabilities. | |
| 653 | |a Computer graphics | ||
| 653 | |a Animation | ||
| 653 | |a Geometry | ||
| 653 | |a Neural networks | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Computer science | ||
| 773 | 0 | |t ProQuest Dissertations and Theses |g (2025) | |
| 786 | 0 | |d ProQuest |t ProQuest Dissertations & Theses Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3275489203/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3275489203/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |