PeRFception: Perception using Radiance Fields

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, 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 overfitting on backgrounds of images. The code and data are publicly available in "https: //postech-cvlab. github. io/PeRFception/".

Authors

• Yoonwoo Jeong
• Seungjoo Shin
• Junha Lee
• Chris Choy
• Anima Anandkumar
• Minsu Cho
• Jaesik Park

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