Lin Juncong, Xiao Pintong, Fu Yinan, Shi Yubin, Wang Hongran, Guo Shihui, He Ying, Lee Tong-Yee
IEEE Trans Vis Comput Graph. 2022 Aug;28(8):2895-2908. doi: 10.1109/TVCG.2020.3041728. Epub 2022 Jun 30.
Color design for 3D indoor scenes is a challenging problem due to many factors that need to be balanced. Although learning from images is a commonly adopted strategy, this strategy may be more suitable for natural scenes in which objects tend to have relatively fixed colors. For interior scenes consisting mostly of man-made objects, creative yet reasonable color assignments are expected. We propose C Assignment, a system providing diverse suggestions for interior color design while satisfying general global and local rules including color compatibility, color mood, contrast, and user preference. We extend these constraints from the image domain to [Formula: see text], and formulate 3D interior color design as an optimization problem. The design is accomplished in an omnidirectional manner to ensure a comfortable experience when the inhabitant observes the interior scene from possible positions and directions. We design a surrogate-assisted evolutionary algorithm to efficiently solve the highly nonlinear optimization problem for interactive applications, and investigate the system performance concerning problem complexity, solver convergence, and suggestion diversity. Preliminary user studies have been conducted to validate the rule extension from 2D to 3D and to verify system usability.
由于需要平衡多种因素，3D室内场景的色彩设计是一个具有挑战性的问题。虽然从图像中学习是一种常用的策略，但这种策略可能更适用于物体颜色相对固定的自然场景。对于主要由人造物体组成的室内场景，需要有创造性且合理的颜色分配。我们提出了C Assignment系统，该系统能为室内色彩设计提供多样的建议，同时满足包括颜色兼容性、色彩氛围、对比度和用户偏好等一般全局和局部规则。我们将这些约束从图像领域扩展到[公式：见文本]，并将3D室内色彩设计表述为一个优化问题。设计以全向方式完成，以确保居住者从可能的位置和方向观察室内场景时能有舒适的体验。我们设计了一种代理辅助进化算法来高效地解决交互式应用中的高度非线性优化问题，并研究了系统在问题复杂度、求解器收敛性和建议多样性方面的性能。已经进行了初步的用户研究，以验证从2D到3D的规则扩展并验证系统的可用性。
