تقرير
SPARF: Neural Radiance Fields from Sparse and Noisy Poses
| العنوان: | SPARF: Neural Radiance Fields from Sparse and Noisy Poses |
|---|---|
| المؤلفون: | Truong, Prune, Rakotosaona, Marie-Julie, Manhardt, Fabian, Tombari, Federico |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | Neural Radiance Field (NeRF) has recently emerged as a powerful representation to synthesize photorealistic novel views. While showing impressive performance, it relies on the availability of dense input views with highly accurate camera poses, thus limiting its application in real-world scenarios. In this work, we introduce Sparse Pose Adjusting Radiance Field (SPARF), to address the challenge of novel-view synthesis given only few wide-baseline input images (as low as 3) with noisy camera poses. Our approach exploits multi-view geometry constraints in order to jointly learn the NeRF and refine the camera poses. By relying on pixel matches extracted between the input views, our multi-view correspondence objective enforces the optimized scene and camera poses to converge to a global and geometrically accurate solution. Our depth consistency loss further encourages the reconstructed scene to be consistent from any viewpoint. Our approach sets a new state of the art in the sparse-view regime on multiple challenging datasets. Comment: Code is released at https://github.com/google-research/sparf. Published at CVPR 2023 as a Highlight |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2211.11738 |
| رقم الأكسشن: | edsarx.2211.11738 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |