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We address the task of aligning CAD models to a video sequence of a complexscene containing multiple objects. Our method can process arbitrary videos andfully automatically recover the 9 DoF pose for each object appearing in it,thus aligning them in a common 3D coordinate frame. The core idea of our methodis to integrate neural network predictions from individual frames with atemporally global, multi-view constraint optimization formulation. Thisintegration process resolves the scale and depth ambiguities in the per-framepredictions, and generally improves the estimate of all pose parameters. Byleveraging multi-view constraints, our method also resolves occlusions andhandles objects that are out of view in individual frames, thus reconstructingall objects into a single globally consistent CAD representation of the scene.In comparison to the state-of-the-art single-frame method Mask2CAD that webuild on, we achieve substantial improvements on the Scan2CAD dataset (from11.6% to 30.7% class average accuracy).

Vid2CAD: CAD Model Alignment using Multi-View Constraints from Videos