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Most problems involving simultaneous localization and mapping can nowadays besolved using one of two fundamentally different approaches. The traditionalapproach is given by a least-squares objective, which minimizes many localphotometric or geometric residuals over explicitly parametrized structure andcamera parameters. Unmodeled effects violating the lambertian surfaceassumption or geometric invariances of individual residuals are encounteredthrough statistical averaging or the addition of robust kernels and smoothnessterms. Aiming at more accurate measurement models and the inclusion ofhigher-order shape priors, the community more recently shifted its attention todeep end-to-end models for solving geometric localization and mapping problems.However, at test-time, these feed-forward models ignore the more traditionalgeometric or photometric consistency terms, thus leading to a low ability torecover fine details and potentially complete failure in corner case scenarios.With an application to dense object modeling from RGBD images, our work aims attaking the best of both worlds by embedding modern higher-order object shapepriors into classical iterative residual minimization objectives. Wedemonstrate a general ability to improve mapping accuracy with respect to eachmodality alone, and present a successful application to real data.

Dense Object Reconstruction from RGBD Images with Embedded Deep Shape Representations