Intrinsic Shape Properties Research Articles

Characterising the effects of shape on tool path motion

Abstract This paper presents a methodology for a priori shape characterisation of tool path motion. Many current methods to describing tool path motion require explicit knowledge of the motion control algorithms implemented on a specific machine. Either a method proposes novel algorithms or requires knowledge of the algorithms currently implemented in a given machine's controller (e.g. minimum jerk, harmonic jerk and minimum jounce). This paper provides a method, that may be applied on any machine, to characterise motion in terms of a tool path's intrinsic shape properties. The characterisation identifies the achievable set of kinematics for a tool path of a given shape without the need for physical machining and a knowledge of the motion control algorithms. The characterisation may be employed in a pre-processing manner to inform the selection of NC file tool path motions. This can therefore help to reduce the material and energy resources being consumed during iterative machining trials and so improve the efficiency and productivity of the manufacturing process.

Bas-Relief Modeling from Normal Layers.

Bas-relief is characterized by its unique presentation of intrinsic shape properties and/or detailed appearance using materials raised up in different degrees above a background. However, many bas-relief modeling methods could not manipulate scene details well. We propose a simple and effective solution for two kinds of bas-relief modeling (i.e., structure-preserving and detail-preserving) which is different from the prior tone mapping alike methods. Our idea originates from an observation on typical 3D models, which are decomposed into a piecewise smooth base layer and a detail layer in normal field. Proper manipulation of the two layers contributes to both structure-preserving and detail-preserving bas-relief modeling. We solve the modeling problem in a discrete geometry processing setup that uses normal-based mesh processing as a theoretical foundation. Specifically, using the two-step mesh smoothing mechanism as a bridge, we transfer the bas-relief modeling problem into a discrete space, and solve it in a least-squares manner. Experiments and comparisons to other methods show that (i) geometry details are better preserved in the scenario with high compression ratios, and (ii) structures are clearly preserved without shape distortion and interference from details.

Subject-Specific Longitudinal Shape Analysis by Coupling Spatiotemporal Shape Modeling with Medial Analysis.

Modeling subject-specific shape change is one of the most important challenges in longitudinal shape analysis of disease progression. Whereas anatomical change over time can be a function of normal aging; anatomy can also be impacted by disease related degeneration. Shape changes to anatomy may also be affected by external structural changes from neighboring structures, which may cause non-linear pose variations. In this paper, we propose a framework to analyze disease related shape changes by coupling extrinsic modeling of the ambient anatomical space via spatiotemporal deformations with intrinsic shape properties from medial surface analysis. We compare intrinsic shape properties of a subject-specific shape trajectory to a normative 4D shape atlas representing normal aging to separately quantify shape changes related to disease. The spatiotemporal shape modeling establishes inter/intra subject anatomical correspondence, which in turn enables comparisons between subjects and the 4D shape atlas, and also quantitative analysis of disease related shape change. The medial surface analysis captures intrinsic shape properties related to local patterns of deformation. The proposed framework simultaneously models extrinsic longitudinal shape changes in the ambient anatomical space, as well as intrinsic shape properties to give localized measurements of degeneration. Six high risk subjects and six controls are randomly sampled from a Huntington's disease image database for quantitative and qualitative comparison.

Flow aligned surfacing of curve networks

We propose a new approach for automatic surfacing of 3D curve networks, a long standing computer graphics problem which has garnered new attention with the emergence of sketch based modeling systems capable of producing such networks. Our approach is motivated by recent studies suggesting that artist-designed curve networks consist of descriptive curves that convey intrinsic shape properties, and are dominated byrepresentative flow linesdesigned to convey the principal curvature lines on the surface. Studies indicate that viewers complete the intended surface shape by envisioning a surface whose curvature lines smoothly blend these flow-line curves. Following these observations we design a surfacing framework that automatically aligns the curvature lines of the constructed surface with the representative flow lines and smoothly interpolates these representative flow, or curvature directions while minimizing undesired curvature variation. Starting with an initial triangle mesh of the network, we dynamically adapt the mesh to maximize the agreement between the principal curvature direction field on the surface and a smoothflow fieldsuggested by the representative flow-line curves. Our main technical contribution is a framework for curvature-based surface modeling, that facilitates the creation of surfaces with prescribed curvature characteristics. We validate our method via visual inspection, via comparison to artist created and ground truth surfaces, as well as comparison to prior art, and confirm that our results are well aligned with the computed flow fields and with viewer perception of the input networks.

True2Form

True2Formis a sketch-based modeling system that reconstructs 3D curves from typical design sketches. Our approach to infer 3D form from 2D drawings is a novel mathematical framework of insights derived from perception and design literature. We note that designers favor viewpoints that maximally reveal 3D shape information, and strategically sketch descriptive curves that convey intrinsic shape properties, such as curvature, symmetry, or parallelism. Studies indicate that viewers apply these properties selectively to envision a globally consistent 3D shape. We mimic this selective regularization algorithmically, by progressively detecting and enforcing applicable properties, accounting for their global impact on an evolving 3D curve network. Balancing regularity enforcement against sketch fidelity at each step allows us to correct for inaccuracy inherent in free-hand sketching. We perceptually validate our approach by showing agreement between our algorithm and viewers in selecting applicable regularities. We further evaluate our solution by: reconstructing a range of 3D models from diversely sourced sketches; comparisons to prior art; and visual comparison to both ground-truth and 3D reconstructions by designers.

Both predictability and familiarity facilitate contour integration

Research has shown that contour detection is impaired in the visual periphery for snake-shaped Gabor contours but not for circular and elliptical contours. This discrepancy in findings could be due to differences in intrinsic shape properties, including shape closure and curvature variation, as well as to differences in stimulus predictability and familiarity. In a detection task using only circular contours, the target shape is both more familiar and more predictable to the observer compared with a detection task in which a different snake-shaped contour is presented on each trial. In this study, we investigated the effects of stimulus familiarity and predictability on contour integration by manipulating and disentangling the familiarity and predictability of snakelike stimuli. We manipulated stimulus familiarity by extensively training observers with one particular snake shape. Predictability was varied by alternating trial blocks with only a single target shape and trial blocks with multiple target shapes. Our results show that both predictability and familiarity facilitated contour integration, which constitutes novel behavioral evidence for the adaptivity of the contour integration mechanism in humans. If familiarity or predictability facilitated contour integration in the periphery specifically, this could explain the discrepant findings obtained with snake contours as compared with circles or ellipses. However, we found that their facilitatory effects did not differ between central and peripheral vision and thus cannot explain that particular discrepancy in the literature.

Synthesis of Spatially and Intrinsically Constrained Curves Using Simulated Annealing

A general technique is presented for automatic generation of B-spline curves in a spatially constrained environment, subject to specified intrinsic shape properties. Spatial constraints are characterized by a distance metric relating points on the curve to polyhedral models of obstacles which the curve should avoid. The shape of the curve is governed by constraints based on intrinsic curve properties such as parametric variation and curvature. To simultaneously address the independent goals of global obstacle avoidance and local control of intrinsic shape properties, curve synthesis is formulated as a combinatorial optimization problem and solved via simulated annealing. Several example applications are presented which demonstrate the robustness of the technique. The synthesis of both uniform and nonuniform B-spline curves is also demonstrated. An extension of the technique to general sculptured surface model synthesis is briefly described, and a preliminary example of simple surface synthesis presented.