Model evolution: An incremental approach to non-rigid structure from motion

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Lifting Object Detection Datasets into 3D (2015, positive): “To account for differences between the 3D exemplars and the object depicted, Su and Guibas [26] choose a few exemplars that appear to be similar to the depicted shape and combine them to produce a single depth map.”
Category-specific object reconstruction from a single image (2014, neutral): “However, the performance of such algorithms has only been explored on simple categories, such as SUV’s [41] or flower petal and clown fish [28].”
Learning 3D Object Categories by Looking Around Them (2017, methodology): “Starting from datasets of unordered images, methods such as [44] and [27] use SFM and manual annotations, such as keypoints in [4, 16], to estimate a rough 3D geometry of objects.”
Sparse Representation for 3D Shape Estimation: A Convex Relaxation Approach (2015, background): “[8], [37], [38], [39]), it has been shown that the low-dimensional linear space is insufficient to model complex shape variation, e.”
Reconstructing PASCAL VOC (2014, background): “These two approaches build upon traditional non-rigid structure from motion methods [5], originally developed for reconstruction from video, and either produce sparse reconstructions [27] or have only been demonstrated on simple classes such as flower petals and clown-fish, while requiring complex m”