MACHINE VISION FOR ANALYZING CONTEMPORARY SCULPTURES - Tamilarasan S, Salai; Singh, Takveer; Pandey, Mukul; Pandey, Sonia; Munjal, Kalpana; Gopinath K; Kumar, Prince; Dixit, Dipti Nitin (ShodhKosh: Journal of Visual and Performing Arts, 2025)
Incorporation of machine vision in the current analysis of sculpture brings a paradigm shift in the art documentation, interpretation, and preservation. This paper presents computational paradigms that incorporate state-of-the-art techniques of computer vision with aesthetic criticalism to assess sculptural structure, materiality and style. The study uses very high resolution images and 3D scans of contemporary sculptures to develop an elaborate pipeline that includes segmentation, normalization and annotation based on metadata. Deep learning models used in methodology include Convolutional Neural Networks (CNNs), 3D-CNNs, and PointNet which are used to extract multidimensional features including surface curvature, geometric and textural complexity. These properties make it easy to classify objects objectively and interpret them subjectively according to the rules of art. Accuracy, mean intersection over union (mIoU), and feature consistency metrics are some of the evaluation metrics that measure the accuracy of the model prediction. Findings reveal an effectiveness of machine vision to identify fin-grained sculptural dimensions, help improve curation, automate restoration, and integrate museums virtually. In addition, the research highlights the interdisciplinary prospects of using art history, computational design, and artificial intelligence to learn more about the meaning and development of art. This study will help fill the gap between visual computing and creative analysis by offering a repeatable structure to digitize and interpret three-dimensional art, which will open the way to new directions of digital heritage, teaching visualization, and aesthetic value analyses with the help of AI.