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About this paper
Nikita Sarawgi, Omey M. Manyar, Satyandra K. Gupta
The authors introduce a deployment-aware view-selection method that learns from local calibration captures which predicted surface points are likely to yield valid depth. It filters likely sensor dropouts while keeping the pretrained view predictor fixed, then selects views expected to add measured surface. On one held-out reflective blue helmet, it reached 98.4% voxel coverage in four captures; nominal selection reached 97.5% in ten and missed the 98% target.
Summary by alphaXiv.
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