NeuralFusion: Online Depth Fusion in Latent Space

• URL: http://arxiv.org/abs/2011.14791v1
• Date: Mon, 30 Nov 2020 13:50:59 GMT
• Title: NeuralFusion: Online Depth Fusion in Latent Space
• Authors: Silvan Weder, Johannes L. Sch\"onberger, Marc Pollefeys, Martin R. Oswald
• Abstract summary: We present a novel online depth map fusion approach that learns depth map aggregation in a latent feature space. Our approach is real-time capable, handles high noise levels, and is particularly able to deal with gross outliers common for photometric stereo-based depth maps.
• Score: 77.59420353185355
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We present a novel online depth map fusion approach that learns depth map aggregation in a latent feature space. While previous fusion methods use an explicit scene representation like signed distance functions (SDFs), we propose a learned feature representation for the fusion. The key idea is a separation between the scene representation used for the fusion and the output scene representation, via an additional translator network. Our neural network architecture consists of two main parts: a depth and feature fusion sub-network, which is followed by a translator sub-network to produce the final surface representation (e.g. TSDF) for visualization or other tasks. Our approach is real-time capable, handles high noise levels, and is particularly able to deal with gross outliers common for photometric stereo-based depth maps. Experiments on real and synthetic data demonstrate improved results compared to the state of the art, especially in challenging scenarios with large amounts of noise and outliers.

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