figure 2. CG line drawings. Recent research has proposed several
ways to create a line drawing from a 3D model (a). we examined
smooth silhouettes (b), suggestive contours (c), geometric ridges
and valleys (d), apparent ridges (e), and image edges (f).
With the myriad line-drawing options now available, it is
natural to ask which are appropriate for a specific situation,
or which more closely resembles what a human would draw.
Recent efforts8, 13, 14 include direct comparisons between
their results and artists’ renderings. However, these comparisons are informal and generally intended to illustrate
the inspiration for the work, not to evaluate the results of the
algorithm. In contrast, the data set presented here makes
possible formal comparisons by directly associating 3D
models, lighting, and camera angles with human drawings.
Hand-drawn data sets. Hand-drawn or hand-annotated
data sets can be useful wherever visual comparison between an algorithm and human performance is desired.
In computer vision, human annotations have been used
as reference data for image segmentation, 15 object recognition, 22 and 3D model segmentation. 3 Segmentation
and line drawing are related but distinct: segmentation
boundaries are an obvious source of lines but generally do
not provide compelling line drawings on their own.
Two recent studies directly investigated drawings by
human artists of 3D models. Isenberg et al. 12 compared
viewers’ perceptions of hand-drawn versus computer-generated pen-and-ink illustrations. Phillips et al. 19 conducted
a study similar to ours, in which artists were asked to draw
synthetic, blobby shapes from a range of prompt types.
Among other differences from that work, our study includes
a separate tracing and registration step that allows greater
accuracy in the analysis of artists’ lines.
2. stuDy DesiGn
The study is designed to capture the relationships between
the locations where human artists draw lines and the mathematical properties of the model’s surface and appearance
at those locations. To achieve this goal in a way that supports
detailed analysis, several important choices must be made:
what drawing style to consider; what models, views, and
lighting conditions to use as prompts; how to present these
prompts to the artists; what instructions to give the artists;
and how to scan and process the drawings. The following
sections describe each of our design decisions in detail.
2. 1. artistic style
The first challenge in designing the study is to decide on
a style of drawing that is narrow enough that all artists
have roughly similar intentions while drawing, yet flexible enough for each artist to exercise individual ingenuity. We balance these goals by focusing on line drawing
that includes only feature lines, with no hatching or shading (examples appear in Figure 5). This choice of style
was made for two reasons. First, it is a simple style that
is familiar to most artists and yet expressive enough to
depict shape. Second, it matches the style generated by
several NPR rendering algorithms recently proposed in the
CG literature7, 13. By asking the artists to draw in the same
style as the computer algorithms, we can learn both about
the human drawings (by using the vocabulary of the algorithms) and the computer drawings (by using statistical
correlations with human tendencies).
We give each artist verbal and written instructions to
make drawings with “lines that convey the shape” of an
object. We do not provide instructions about whether lines
should represent shape features, lighting features, or anything else. However, we specifically ask the artists to refrain
from including lines that represent area shading or tone
features, such as stippling or hatching.
2. 2. Prompt selection
A second design decision is to select 3D models and rendering parameters to use when producing prompts (images
depicting a shape for the artists to draw).