From Contour to Texture: Static Texture Flow is a Strong Cue to Surface Shape

Considerable evidence suggests that a viewer's perception of the 3D shape of a polygonally-defined object can be significantly affected (either masked or enhanced) by the presence of a surface texture pattern. However, investigations into the specific mechanisms of texture's effect on shape perception are still ongoing and the question of how to design and apply a texture pattern to a surface in order to best facilitate shape perception remains open. Recently, we have suggested that, for anisotropic texture patterns, the accuracy of shape judgments may be significantly affected by the orientation of the surface texture pattern anisotropy with respect to the principal directions of curvature over the surface. However, it has been difficult, until this time, to conduct controlled studies specifically investigating the effect of texture orientation on shape perception because there has been no simple and reliable method for texturing an arbitrary doubly curved surface with a specified input pattern such that the dominant orientation of the pattern everywhere follows a predefined directional vector field over the surface, while seams and projective distortion of the pattern are avoided. In this paper, we present a straightforward and highly efficient method for achieving such a texture and describe how it can potentially be used to enhance shape representation. Specifically, we describe a novel, efficient, automatic algorithm for seamlessly synthesizing, from a sample 2D pattern, a high resolution fitted surface texture in which the dominant orientation of the pattern locally follows a specified vector field over the surface at a per-pixel level and in which seams, projective distortion, and repetition artifacts in the texture pattern are nearly completely avoided. We demonstrate the robustness of our method with a variety of texture swatches applied to standard graphics data sets and we explain how our method can be used to facilitate research in the perception of shape from texture.

The human visual system is tasked with recovering the different physical sources of optical structure that generate our retinal images. Separate research has focused on understanding how the visual system estimates (a) environmental sources of image structure and (b) blur induced by the eye's limited focal range, but little is known about how the visual system distinguishes environmental sources from optical defocus. Here, we present evidence that this is a fundamental perceptual problem and provide insights into how and when the visual system succeeds and fails in solving it. We show that fully focused surface shading can be misperceived as defocused and that optical blur can be misattributed to the material properties and shape of surfaces. We further reveal how these misperceptions depend on the relationship between shading gradients and sharp contours, and conclude that computations of blur are inherently linked to computations of surface shape, material, and illumination.

Photogeometric Cues to Perceived Surface Shading

Perception of three-dimensional shape from texture is based on patterns of oriented energy

3D shape of the weld pool surface can be used to infer the penetration state of the weld pool, which has an importance influence on weld quality. In this paper, a novel stereo camera system that used only one camera and a biprism placed in front of the lens was applied to capture virtual image pair of the weld pool surface. The most advantage of the system setup is that corresponding points lie on the same scan line automatically, i.e. the stereo image pair is self rectified. The calibration of the system was done using plane pattern by Zhang’s algorithm and the standard SSD stereo matching algorithm was applied to find the corresponding points in the left and right image. The reconstructed 3D shape of the weld pool surface during base current period of P-GMAW by the system has shown that the technique has potential application for control of weld process

The effect of age upon the perception of 3-D shape from motion

This study introduces and examines the principle of accentuation as a novel mechanism in perceptual organization, analyzing its effects through the framework of Grossberg’s Adaptive Resonance Theory (ART). We demonstrate that localized accentuators, manifesting as minimal dissimilarities or discontinuities, can significantly modulate global perceptions, inducing illusions of geometric distortion, orientation shifts, and apparent motion. Through a series of phenomenological experiments, we establish that accentuation can supersede classical Gestalt principles, influencing figure-ground segregation, shape perception, and lexical processing. Our findings suggest that accentuation functions as an autonomous organizing principle, leveraging salience-driven attentional capture to generate perceptual effects. We then apply the ART model to elucidate these phenomena, focusing on its core constructs of complementary computing, boundary–surface interactions, and resonant states. Specifically, we show how accentuation-induced asymmetries in boundary signals within the boundary contour system (BCS) can propagate through laminar cortical circuits, biasing figure-ground assignments and shape representations. The interaction between these biased signals and top–down expectations, as modeled by ART’s resonance mechanisms, provides a neurally plausible account for the observed illusions. This integration of accentuation effects with ART offers novel insights into the neural substrates of visual perception and presents a unifying theoretical framework for a diverse array of perceptual phenomena, bridging low-level feature processing with high-level cognitive representations.

A textural pattern is used to obtain physical and optical functions, and it is also possible to improve an ornamental design. This study describes a new machining strategy that utilizing patch division milling, In order to obtain regularly aligned micro dimples on surface by a micro ball-end milling. In previous research, triangle patch and accumulated truncated triangle pyramid patch can be formed with regular patterns for the functions on machined surface. To fit the shape of the object's surface, experimental results of the proposed method as applied to spherical and free-form surfaces are described.

An adaptive approach for texture enhancement based on a fractional differential operator with non-integer step and order

Surface representation as a boundary-valued problem of partial differential equations (PDE) is an important topic of computer graphics and computer aided design. In existing references, various free-from surfaces were created with a fourth order PDE which is only able to meet the tangential conditions at the surface boundaries. The need for a sixth order PDE in surface modeling arises in two situations: one is to generate surfaces with curvature continuity; and the other is to use curvature values as a user handle for surface shape manipulation. In this chapter, we introduce such a sixth order PDE for free-form surface generation and develop a finite difference method to solve this PDE. We also investigate the effects of boundary curvature and the vector-valued shape parameters on the surface shape.

In order to evaluate the influence of the initial plastic anisotropy in the excavation of a tunnel, a “bubble” bounding surface model for structured soils, formulated by Kavvadas and Belokas (Proc. 10th IACMAG Conf., 2001), was implemented in the explicit finite difference code FLAC. Two different initial stress (K 0) conditions were considered. The size and shape of the initial bounding surfaces were specified to be consistent with the initial stress field. The distorted and rotated shape of the bounding surface, supported by experimental results, defines the anisotropy of shear strength, which is shown to have a significant influence on the displacements. There is also considerable sensitivity of the soil model to the initial stress field.

Uniform and decoupled shape effects on the maximally dense random packings of hard superellipsoids

Cue combination rules have often been applied to the perception of surface shape but not to judgements of object location. Here, we used immersive virtual reality to explore the relationship between different cues to distance. Participants viewed a virtual scene and judged the change in distance of an object presented in two intervals, where the scene changed in size between intervals (by a factor of between 0.25 and 4). We measured thresholds for detecting a change in object distance when there were only 'physical' (stereo and motion parallax) or 'texture-based' cues (independent of the scale of the scene) and used these to predict biases in a distance matching task. Under a range of conditions, in which the viewing distance and position of the target relative to other objects was varied, the ratio of 'physical' to 'texture-based' thresholds was a good predictor of biases in the distance matching task. The cue combination approach, which successfully accounts for our data, relies on quite different principles from those underlying traditional models of 3D reconstruction.

The success of neural fields for 3D vision tasks is now indisputable. Following this trend, several methods aiming for visual localization (e.g., SLAM) have been proposed to estimate distance or density fields using neural fields. However, it is difficult to achieve high localization performance by only density fields-based methods such as Neural Radiance Field (NeRF) since they do not provide density gradient in most empty regions. On the other hand, distance field-based methods such as Neural Implicit Surface (NeuS) have limitations in objects’ surface shapes. This paper proposes Neural Distance-Density Field (NeDDF), a novel 3D representation that reciprocally constrains the distance and density fields. We extend distance field formulation to shapes with no explicit boundary surface, such as fur or smoke, which enable explicit conversion from distance field to density field. Consistent distance and density fields realized by explicit conversion enable both robustness to initial values and high-quality registration. Furthermore, the consistency between fields allows fast convergence from sparse point clouds. Experiments show that NeDDF can achieve high localization performance while providing comparable results to NeRF on novel view synthesis. The code is available at https://github.com/ueda0319/neddf.

Spatiotemporal boundary formation (SBF) is the perception of form, global motion, and continuous boundaries from relations of discrete changes in local texture elements (Shipley and Kellman, 1994). In two experiments, small, circular elements underwent small displacements whenever an edge of an invisible (virtual) object passed over them. Unlike previous studies that examined only rigidly translating objects, we tested virtual objects whose properties changed continuously. Experiment 1 tested rigid objects that changed in orientation, scale, and velocity. Experiment 2 tested objects that transformed non-rigidly taking on a series of shapes. Robust SBF occurred for all of the rigid transformations tested, as well as for non-rigid virtual objects, producing the perception of continuously bounded, smoothly deforming shapes. These novel illusions involve perhaps the most extreme cases of visual perception of continuous boundaries and shape from minimal information. They show that SBF encompasses a wider range of illusory phenomena than previously understood, and they present substantial challenges for existing models of SBF.