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- W4300978466 abstract "We devise a graph attention network-based approach for learning a scene triangle mesh representation in order to estimate an image camera position in a dynamic environment. Previous approaches built a scene-dependent model that explicitly or implicitly embeds the structure of the scene. They use convolution neural networks or decision trees to establish 2D/3D-3D correspondences. Such a mapping overfits the target scene and does not generalize well to dynamic changes in the environment. Our work introduces a novel approach to solve the camera relocalization problem by using the available triangle mesh. Our 3D-3D matching framework consists of three blocks: (1) a graph neural network to compute the embedding of mesh vertices, (2) a convolution neural network to compute the embedding of grid cells defined on the RGB-D image, and (3) a neural network model to establish the correspondence between the two embeddings. These three components are trained end-to-end. To predict the final pose, we run the RANSAC algorithm to generate camera pose hypotheses, and we refine the prediction using the point-cloud representation. Our approach significantly improves the camera pose accuracy of the state-of-the-art method from $0.358$ to $0.506$ on the RIO10 benchmark for dynamic indoor camera relocalization." @default.
- W4300978466 created "2022-10-04" @default.
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- W4300978466 date "2022-09-29" @default.
- W4300978466 modified "2023-10-16" @default.
- W4300978466 title "Graph Attention Network for Camera Relocalization on Dynamic Scenes" @default.
- W4300978466 doi "https://doi.org/10.48550/arxiv.2209.15056" @default.
- W4300978466 hasPublicationYear "2022" @default.
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