标题: A Hybrid Object/Image Space Approach for Efficient and Robust Line Drawings [打印本页] 作者: 彬彬 时间: 2012-1-5 08:22 标题: A Hybrid Object/Image Space Approach for Efficient and Robust Line Drawings 1 Introduction
Line drawings are an effective way to convey shapes in a relatively
succinct manner by ignoring the less important or distracting details.
In the past decade, many promising computer-generated line
drawing algorithms have been proposed, which can be roughly classified
into two categories: object-space and image-space.
The object-space algorithms, e.g., suggestive contours [DeCarlo
et al. 2003], apparent ridges [Judd et al. 2007], PELs [Xie et al.
2007], demarcating curves [Kolomenkin et al. 2008], Laplacian
lines [Zhang et al. 2011], etc, adopt a two-pass algorithmic framework:
in the first pass, certain geometric properties (such as curvatures,
principal directions and their derivatives) are computed for
each vertex and the geometry of the feature lines are extracted; then
the extracted lines are rendered in the second pass. In spite of its
popularity, this two-pass framework usually requires that the input
model is smooth and has good triangulation quality, since computing
high order geometric properties using discrete differential
geometry is very sensitive to mesh tessellation and noises. Thus,
for real-world models obtained from 3D scanning, a careful preprocessing
(e.g., denoising, remeshing, etc) must be done before
applying the line drawing algorithms. The image-space algorithms
(e.g., [Lee et al. 2007]) are usually efficient since they avoid the expensive
computations of differential properties on surfaces. However,
image-space algorithms suffer pixel-level artifacts and are difficult
for shape stylization.
This paper presents a new hybrid approach for efficient and robust
line drawing on general triangle meshes. Our method naturally integrates
object- and image-spaces in that it computes the geometric
features in the object space and then adopts a simple fragment
shader to render the lines in the image space. As a result, our algorithm
is more efficient than the existing object-space approaches,
since we bypass the process of line geometry extraction, which
is normally the most time-consuming operation in traditional line
drawing algorithms. On the other hand, our algorithm is also more
robust than the conventional image-space techniques, since the geometric
features are extracted in the object space, which is independent
of the image resolution.
Our approach is strongly inspired by the Difference-of-Gaussian
(DoG) image edge detector. Given a grey-scale image, the DoG
edge detector first convolutes it with two Gaussian filters with different
kernel sizes. Then subtracting one image from the other preserves
spatial information that lies between the range of frequencies
that are preserved in the two blurred images. Thus, the DoG operator
is a band-pass filter that discards all but a handful of spatial
frequencies that are present in the original grayscale image.
A na¨ıve approach for 3D line drawings is to apply image edge detectors
(e.g., DoG detector, Canny edge detector, etc) to the rendered
diffuse shading image. However, these approaches severely
suffer the pixel level artifacts and the quality of resulting drawings
are usually poor. In this sketch, we present an elegant algorithm
to generalize DoG operator to 3D models. We show that the DoG
of illumination can be decomposed into the view-independent and
view-dependent components, where the former, the most computational
expensive part, can be completely precomputed. Furthermore,
the run-time line drawing can be done with very simple arithmetic
calculations using the GPU.作者: 彬彬 时间: 2012-1-13 10:57