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PeRFception: Perception using Radiance Fields
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Web of Science
Cited 4 time in Scopus
- Authors
- Issue Date
- 2022
- Citation
- Advances in Neural Information Processing Systems, Vol.35
- Abstract
- 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 radiance fields datasets for perception tasks, called the PeRFception dataset, 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 this radiance fields format as input and also propose a novel augmentation technique to avoid over-fitting on backgrounds of images. The code and data are publicly available in https://postech-cvlab.github.io/PeRFception/.
- ISSN
- 1049-5258
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