Attention for Rendered 3D Shapes
of Lyon, CNRS, LIRIS, France
of Haute-Alsace, LMIA, France
of Strasbourg, CNRS, ICube, France
of Poitiers, CNRS, XLIM-SIC, France
In Computer Graphics Forum (Proceedings of
We conducted eye tracking
experiments on rendered 3D objects. The human saliency information is
mapped on the 3D meshes (in the form of fixation density maps) and
serves to study which factors influence human attention and to evaluate
state-of-the-art saliency algorithms. At the bottom we show the Pearson
correlation between saliency maps from humans and algorithms
Understanding the attentional behavior of the human
visual system when visualizing a rendered 3D shape is of great
importance for many computer graphics applications. Eye tracking
remains the only solution to explore this complex cognitive mechanism.
Unfortunately, despite the large number of studies dedicated to images
and videos, only a few eye tracking experiments have been conducted
using 3D shapes. Thus, potential factors that may influence the human
gaze in the specific setting of 3D, are still to be understood.
In this work, we conduct two eye-tracking experiments involving 3D
shapes, with both static and time-varying camera positions. We propose
a method for mapping eye fixations (i.e., where humans gaze) onto the
3D shapes with the aim to produce a benchmark of 3D meshes with
fixation density maps, which is publicly available. First, the
collected data is used to study the influence of shape, camera
position, material and illumination on visual attention. We find that
material and lighting have a significant influence on attention, as
well as the camera path in the case of dynamic scenes. Then, we compare
the performance of four representative state-of-the-art mesh saliency
models in predicting ground-truth fixations using two different
metrics. We show that, even combined with a center-bias model, the
performance of 3D saliency algorithms remains poor at predicting human
fixations. To explain their weaknesses, we provide a qualitative
analysis of the main factors that attract human attention. We finally
provide a quantitative comparison of human-eye fixations and Schelling
points and show that their correlation is weak.
All data can be downloaded via the following links
- 32 full resolution 3D shapes (OBJ format), originaly used to acquire
the fixation maps and compute the saliency from automatic algorithms.
(13MB) - 32 shapes with decreased
resolution (OBJ format), used to compute the statistics.
(2MB) - ASCII files with
per-vertex fixation values. 96 fixation files, one for each view of
(27MB) - ASCII files with per-vertex saliency values. 384 saliency
files, one for each shape, algorithm (Lee, Leifman, Song and
Tasse) and blurring factor (3).
Centricity and visibility maps
(32MB) - ASCII files with per-vertex visibility values (96 files) and
ASCII files with per-vertex centricity values (for 13 different
Gaussian radius) (1248 files).
We are deeply grateful to Flora Ponjou Tasse,
Leifman, Ran Song and Chang Ha Lee for kindly running their saliency
algorithms on our dataset (or providing their source code).