LiveNVS: Neural View Synthesis on Live RGB-D Streams

• URL: http://arxiv.org/abs/2311.16668v2
• Date: Wed, 29 Nov 2023 11:29:45 GMT
• Title: LiveNVS: Neural View Synthesis on Live RGB-D Streams
• Authors: Laura Fink, Darius R\"uckert, Linus Franke, Joachim Keinert, Marc Stamminger
• Abstract summary: We present LiveNVS, a system that allows for neural novel view synthesis on a live RGB-D input stream. LiveNVS achieves state-of-the-art neural rendering quality of unknown scenes during capturing.
• Score: 4.717325308876748
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing real-time RGB-D reconstruction approaches, like Kinect Fusion, lack real-time photo-realistic visualization. This is due to noisy, oversmoothed or incomplete geometry and blurry textures which are fused from imperfect depth maps and camera poses. Recent neural rendering methods can overcome many of such artifacts but are mostly optimized for offline usage, hindering the integration into a live reconstruction pipeline. In this paper, we present LiveNVS, a system that allows for neural novel view synthesis on a live RGB-D input stream with very low latency and real-time rendering. Based on the RGB-D input stream, novel views are rendered by projecting neural features into the target view via a densely fused depth map and aggregating the features in image-space to a target feature map. A generalizable neural network then translates the target feature map into a high-quality RGB image. LiveNVS achieves state-of-the-art neural rendering quality of unknown scenes during capturing, allowing users to virtually explore the scene and assess reconstruction quality in real-time.

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