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Con-DSO: Learning Short-Horizon Consistency Priors for RGB-D Direct Sparse Odometry
RGB-D direct visual odometry (VO) benefits from metric depth measurements but often degrades in the presence of dynamic objects, occlusions, illumination changes, and unreliable depth, which violate the photometric and geometric consistency assumptions of direct alignment. We propose Con-DSO, a cons
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