Finding Good Configurations of Planar Primitives in Unorganized Point Clouds

Abstract

We present an algorithm for detecting planar primitives from unorganized 3D point clouds. Departing from an initial configuration, the algorithm refines both the continuous plane parameters and the discrete assignment of input points to them by seeking high fidelity, high simplicity and high completeness. Our key contribution relies upon the design of an exploration mechanism guided by a multiobjective energy function. The transitions within the large solution space are handled by five geometric operators that create, remove and modify primitives. We demonstrate the potential of our method on a variety of scenes, from organic shapes to man-made objects, and sensors, from multiview stereo to laser. We show its efficacy with respect to existing primitive fitting approaches and illustrate its applicative interest in compact mesh reconstruction, when combined with a plane assembly method.

Deep scene-scale material estimation from multi-view indoor captures

“…To obtain high quality re-renderings we need good geometry, justifying our design choice of using retopology. In future work, it may be possible to adapt previous methods (e.g., [55,56]) to provide geometry that corrects these errors for flat surfaces, but it would be necessary to preserve the relatively well reconstructed irregular objects (such as the fruit on the table).…”