Authors:
Ole Wegen
1
;
Josafat-Mattias Burmeister
1
;
Max Reimann
2
;
Rico Richter
1
and
Jürgen Döllner
2
Affiliations:
1
University of Potsdam, Germany
;
2
Hasso-Plattner-Institute, University of Potsdam, Germany
Keyword(s):
3D Point Clouds, Non-Photorealistic Rendering, Segmentation, Image-Based Artistic Rendering.
Abstract:
3D point clouds are a widely used representation for surfaces and object geometries. However, their visualization can be challenging due to point sparsity and acquisition inaccuracies, leading to visual complexity and ambiguity. Non-photorealistic rendering (NPR) addresses these challenges by using stylization techniques to abstract from certain details or emphasize specific areas of a scene. Although NPR effectively reduces visual complexity, existing approaches often apply uniform styles across entire point clouds, leading to a loss of detail or saliency in certain areas. To address this, we present a novel segment-based NPR approach for point cloud visualization. Utilizing prior point cloud segmentation, our method applies distinct rendering styles to different segments, enhancing scene understanding and directing the viewer’s attention. Our emphasis lies in integrating aesthetic and expressive elements through image-based artistic rendering, such as watercolor or cartoon filterin
g. To combine the per-segment images into a consistent final image, we propose a user-controllable depth inpainting algorithm. This algorithm estimates depth values for pixels that lacked depth information during point cloud rendering but received coloration during image-based stylization. Our approach supports real-time rendering of large point clouds, allowing users to interactively explore various artistic styles.
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