MeTACAST: Target- and Context-aware Spatial Selection in VR

TL;DR

This work tackles the challenge of selecting meaningful regions in large, unstructured 3D point clouds within VR. It introduces MeTAPoint, MeTABrush, and MeTAPaint as density-aware, target- and context-aware selection techniques that adjust inputs toward local density maxima and support interactive post-selection thresholding. The authors implement GPU-accelerated density estimation and iso-surface extraction and validate the approach through a controlled user study against a Baseline method, demonstrating improved accuracy and efficiency across diverse data features. The paper also provides practical guidelines for choosing 3D spatial selection techniques based on interaction environment and data characteristics, enabling robust exploratory analysis in immersive analytics scenarios.

Abstract

We propose three novel spatial data selection techniques for particle data in VR visualization environments. They are designed to be target- and context-aware and be suitable for a wide range of data features and complex scenarios. Each technique is designed to be adjusted to particular selection intents: the selection of consecutive dense regions, the selection of filament-like structures, and the selection of clusters -- with all of them facilitating post-selection threshold adjustment. These techniques allow users to precisely select those regions of space for further exploration -- with simple and approximate 3D pointing, brushing, or drawing input -- using flexible point- or path-based input and without being limited by 3D occlusions, non-homogeneous feature density, or complex data shapes. These new techniques are evaluated in a controlled experiment and compared with the Baseline method, a region-based 3D painting selection. Our results indicate that our techniques are effective in handling a wide range of scenarios and allow users to select data based on their comprehension of crucial features. Furthermore, we analyze the attributes, requirements, and strategies of our spatial selection methods and compare them with existing state-of-the-art selection methods to handle diverse data features and situations. Based on this analysis we provide guidelines for choosing the most suitable 3D spatial selection techniques based on the interaction environment, the given data characteristics, or the need for interactive post-selection threshold adjustment.

Figures (42)

• Figure 1: MeTAPoint: (a) the user points at the target cluster (red); (b) we derive the closest maximum point (blue) and density threshold (schematic representation); (c) we compute the selection volume; (d) the user drags the controller to adjust the density threshold; (e, f) we recompute the density threshold and selection volume.
• Figure 2: MeTABrush: (a) the user draws a 3D stroke (red); (b) the stroke points (red) are extracted; by following the direction of gradient (blue arrow), we identify the local maximum points (blue); (c) we construct a tunnel-like volume (yellow dotted region) based on the MaxLine with the radius $R$; $V_{\mathrm{init}}$ is derived (blue dotted line); (d) the final selection.
• Figure 3: MeTAPaint: (a) the user draws a 3D stroke (red) near the black cluster, some parts of the input are located near the gray one; (b) the stroke is split into multiple points (red), which flow towards local density maxima (blue and gray points) along the direction of the gradient; (c) the black cluster is selected since it receives most seeds.
• Figure 4: Datasets we used in our study: (a) Disk, (b) Rings, (c) Shell, (d) Strings, and (e--g) Filaments.
• Figure 5: The predicted results are determined by analyzing the data features and the principle of the selection methods: red (relatively good performance), blue (relatively poor performance).