Evaluation of Depth of Field for depth perception in DVR

Abstract

In this paper, we present a user study on the use of Depth of Field for depth perception in Direct Volume Rendering. Direct Volume Rendering with Phong shading and perspective projection is used as the baseline. Depth of Field is then added to see its impact on the correct perception of ordinal depth. Accuracy and response time are used as the metrics to evaluate the usefulness of Depth of Field. The on site user study has two parts: static and dynamic. Eye tracking is used to monitor the gaze of the subjects. From our results we see that though Depth of Field does not act as a proper depth cue in all conditions, it can be used to reinforce the perception of which feature is in front of the other. The best results (high accuracy & fast response time) for correct perception of ordinal depth is when the front feature (out of the users were to choose from) is in focus and perspective projection is used.

Evaluating the perception of semi-transparent structures in direct volume rendering techniques

“…When considering previous studies, two types of perception are in focus: depth perception and shape perception. While Boucheny et al have evaluated depth perception of DVR with a three-alternative forced-choice test [2009], more recent studies also collect continuous feedback with point-based judgment trials [Lindemann and Ropinski 2011;Grosset et al 2013]. Our study is inspired by these evaluations, and we use similar depth trials: ordinal and absolute depth judgments.…”

A crowdsourcing system for integrated and reproducible evaluation in scientific visualization

“…In visualization research, these types of studies can be used to address perceptual and comprehensibility questions [20], whereby low-level tasks such as compare, contrast, associate, distinguish, rank, cluster, correlate or categorize can be considered [40]. Unfortunately, the number of quantitative user evaluations is still rather low [23], despite the fact that these evaluations can be used to address a broad variety of research questions, reaching from aesthetics [41], over spatial comprehension [18], and visual averaging [17] to memorability [3]. Crowdsourcing.…”

“…To identify, which schemes an evaluation system should support, we have reviewed quantitative user evaluations earlier reported in the visualization literature. Based on this analysis we have decided to confine ourselves to evaluations which include static images only, as they have been described in Kosara's overview [27], and as they are facilitated in many recent publications about evaluations, for example, [31,45,18,17,3]. While not all publications describe the used questionnaires in all necessary detail, in most cases we were able to reconstruct the underlying scheme based on the result analysis.…”

“…In other words, N lists of trials, where N is the number of levels of the factor to be evaluated, have to be generated. Another important trial scheme is the within-subjects design, as used in several other studies [31,18]. In this scheme, all participants will perform all available trials and therefore also be subject to all levels of the factor.…”

“…In many cases, this correctness might be encoded in a pixel's color, either in the stimuli itself but mostly in one of the annotation layers. We found this especially useful for evaluations exploiting markers, were the distance to the camera [18], surface normals [45], or object size [31] have to be judged. In some occasions the correctness cannot be specified a priori or annotation layers are not available, for example when reusing stimuli from old user evaluations, then the correct value can be stored in the stimulus as a label.…”