Atomic growing for grid alignment

Abstract

Detecting and aligning structured signals such as point grids plays a fundamental role in many signal processing applications. Joint determination of non-grid points and estimation of non-linear spatial distortions applied to the grid is a key challenge for grid alignment. This paper proposes a candidate solution. The method described herein starts from a small nearly regular region found in the point set and then expands the list of candidate points included in the grid. The proposed method was tested on geometrically transformed point sets and sets of locations derived from imagery of 3D prints. It is shown that a low-complexity grid alignment method can nonetheless achieve high grid alignment accuracy.

Scale-adaptive local intentional surface feature detection

“…These have been used to mark circuit components [16], and in a recent application, have been applied as physical scene markers to augment LiDAR measurements [17]. Our previous work [10] proposed a scheme that uses a 2D grid-like point pattern to encode a message, as is shown in Figure 2, which we have extended for rendering onto 3D surfaces [11]. The encoding scheme determines the surface positions at which the deformations are placed.…”

“…In particular, it is necessary to detect these intentional features in the imagery, i.e., a 2D feature localization algorithm is needed. We adapted the SIFT algorithm for the intentional features to serve this purpose; for details relating to data encoding and feature alignment see [11]. The process of creating, rendering and later on interpreting these intentional features is referred to as surface coding.…”