Direct-PoseNet: Absolute Pose Regression with Photometric Consistency

• URL: http://arxiv.org/abs/2104.04073v1
• Date: Thu, 8 Apr 2021 21:10:18 GMT
• Title: Direct-PoseNet: Absolute Pose Regression with Photometric Consistency
• Authors: Shuai Chen, Zirui Wang, Victor Prisacariu
• Abstract summary: We present a relocalization pipeline, which combines an absolute pose regression network with a novel view synthesis based direct matching module. Our network Direct-PoseNet achieves state-of-the-art performance among all other single-image APR methods on the 7-Scenes benchmark and the LLFF dataset.
• Score: 16.60612964943876
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a relocalization pipeline, which combines an absolute pose regression (APR) network with a novel view synthesis based direct matching module, offering superior accuracy while maintaining low inference time. Our contribution is twofold: i) we design a direct matching module that supplies a photometric supervision signal to refine the pose regression network via differentiable rendering; ii) we modify the rotation representation from the classical quaternion to SO(3) in pose regression, removing the need for balancing rotation and translation loss terms. As a result, our network Direct-PoseNet achieves state-of-the-art performance among all other single-image APR methods on the 7-Scenes benchmark and the LLFF dataset.

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