تقرير
PeRFception: Perception using Radiance Fields
| العنوان: | PeRFception: Perception using Radiance Fields |
|---|---|
| المؤلفون: | Jeong, Yoonwoo, Shin, Seungjoo, Lee, Junha, Choy, Christopher, Anandkumar, Animashree, Cho, Minsu, Park, Jaesik |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | The recent progress in implicit 3D representation, i.e., Neural Radiance Fields (NeRFs), has made accurate and photorealistic 3D reconstruction possible in a differentiable manner. This new representation can effectively convey the information of hundreds of high-resolution images in one compact format and allows photorealistic synthesis of novel views. In this work, using the variant of NeRF called Plenoxels, we create the first large-scale implicit representation datasets for perception tasks, called the PeRFception, which consists of two parts that incorporate both object-centric and scene-centric scans for classification and segmentation. It shows a significant memory compression rate (96.4\%) from the original dataset, while containing both 2D and 3D information in a unified form. We construct the classification and segmentation models that directly take as input this implicit format and also propose a novel augmentation technique to avoid overfitting on backgrounds of images. The code and data are publicly available in https://postech-cvlab.github.io/PeRFception . Comment: Project Page: https://postech-cvlab.github.io/PeRFception/ |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2208.11537 |
| رقم الأكسشن: | edsarx.2208.11537 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |