Fast and Lightweight Scene Regressor for Camera Relocalization

• URL: http://arxiv.org/abs/2212.01830v1
• Date: Sun, 4 Dec 2022 14:41:20 GMT
• Title: Fast and Lightweight Scene Regressor for Camera Relocalization
• Authors: Thuan B. Bui, Dinh-Tuan Tran, and Joo-Ho Lee
• Abstract summary: Estimating the camera pose directly with respect to pre-built 3D models can be prohibitively expensive for several applications. This study proposes a simple scene regression method that requires only a multi-layer perceptron network for mapping scene coordinates. The proposed approach uses sparse descriptors to regress the scene coordinates, instead of a dense RGB image.
• Score: 1.6708069984516967
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Camera relocalization involving a prior 3D reconstruction plays a crucial role in many mixed reality and robotics applications. Estimating the camera pose directly with respect to pre-built 3D models can be prohibitively expensive for several applications with limited storage and/or communication bandwidth. Although recent scene and absolute pose regression methods have become popular for efficient camera localization, most of them are computation-resource intensive and difficult to obtain a real-time inference with high accuracy constraints. This study proposes a simple scene regression method that requires only a multi-layer perceptron network for mapping scene coordinates to achieve accurate camera pose estimations. The proposed approach uses sparse descriptors to regress the scene coordinates, instead of a dense RGB image. The use of sparse features provides several advantages. First, the proposed regressor network is substantially smaller than those reported in previous studies. This makes our system highly efficient and scalable. Second, the pre-built 3D models provide the most reliable and robust 2D-3D matches. Therefore, learning from them can lead to an awareness of equivalent features and substantially improve the generalization performance. A detailed analysis of our approach and extensive evaluations using existing datasets are provided to support the proposed method. The implementation detail is available at https://github.com/aislab/feat2map

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