  We propose an approach for 3D reconstruction and segmentation of a single
object placed on a flat surface from an input video. Our approach is to perform
dense depth map estimation for multiple views using a proposed objective
function that preserves detail. The resulting depth maps are then fused using a
proposed implicit surface function that is robust to estimation error,
producing a smooth surface reconstruction of the entire scene. Finally, the
object is segmented from the remaining scene using a proposed 2D-3D
segmentation that incorporates image and depth cues with priors and
regularization over the 3D volume and 2D segmentations. We evaluate 3D
reconstructions qualitatively on our Object-Videos dataset, comparing to
fusion, multiview stereo, and segmentation baselines. We also quantitatively
evaluate the dense depth estimation using the RGBD Scenes V2 dataset [Henry et
al. 2013] and the segmentation using keyframe annotations of the Object-Videos
dataset.
