Abstracting complex 3D shapes with parsimonious part-based representations has been a long standing goal in computer vision. This paper presents a learning-based solution to this problem which goes beyond the traditional 3D cuboid representation by exploiting superquadrics as atomic elements. We demonstrate that superquadrics lead to more expressive 3D scene parses while being easier to learn than 3D cuboid representations. Moreover, we provide an analytical solution to the Chamfer loss which avoids the need for computational expensive reinforcement learning or iterative prediction. Our model learns to parse 3D objects into consistent superquadric representations without supervision. Results on various ShapeNet categories as well as the SURREAL human body dataset demonstrate the flexibility of our model in capturing fine details and complex poses that could not have been modelled using cuboids.
Superquadrics are a parametric family of surfaces that can be used to describe cubes, cylinders, spheres, octahedra, ellipsoids @cite_28 . In contrast to geons @cite_44 , superquadric surfaces can be described using a fairly simple parameterization. In contrast to generalized cylinders @cite_44 , superquadrics are able to represent a larger variety of shapes. See fig:superquadrics for an illustration of the shape space.
@cite_28 @cite_44
2023751161 2125756925
A new and powerful family of parametric shapes extends the basic quadric surfaces and solids, yielding a variety of useful forms. The perceptual recognition of objects is conceptualized to be a process in which the image of the input is segmented at regions of deep concavity into simple volumetric components, such as blocks, cylinders, wedges, and cones. The fundamental assumption of the proposed theory, recognition-by-components (RBC), is that a modest set of components [N probably ≤ 36] can be derived from contrasts of five readily detectable properties of edges in a 2-dimensional image: curvature, collinearity, symmetry, parallelism, and cotermination. The detection of these properties is generally invariant over viewing position and image quality and consequently allows robust object perception when the image is projected from a novel viewpoint or degraded. RBC thus provides a principled account of the heretofore undecided relation between the classic principles of perceptual organization and pattern recognition: The constraints toward regularization (Pragnanz) characterize not the complete object but the object's components. A principle of componential recovery can account for the major phenomena of object recognition: If an arrangement of two or three primitive components can be recovered from the input, objects can be quickly recognized even when they are occluded, rotated in depth, novel, or extensively degraded. The results from experiments on the perception of briefly presented pictures by human observers provide empirical support for the theory.