arXiv:2407.17205v1 Announce Type: new
Abstract: The technology to capture, create, and use three-dimensional (3D) models has become increasingly accessible in recent years.
With increasing numbers of use cases for 3D models and collections of rapidly increasing size, better methods to analyze the content of 3D models are required.
While previously proposed 3D model collections for research purposes exist, these often contain only untextured geometry and are typically designed for a specific application, which limits their use in quantitative evaluations of modern 3D model analysis methods.
In this paper, we introduce the Sketchfab 3D Creative Commons Collection (S3D3C), a new 3D model research collection consisting of 40,802 creative commons licensed models downloaded from the 3D model platform Sketchfab.
By including popular freely available models with a wide variety of technical properties, such as textures, materials, and animations, we enable its use in the evaluation of state-of-the-art geometry-based and view-based 3D model analysis and retrieval techniques.
Expert Commentary: The Advancements in 3D Model Analysis and Retrieval Techniques
Over the past few years, the accessibility of technology to capture, create, and use three-dimensional (3D) models has significantly improved. This has led to a vast increase in the use cases for 3D models, resulting in collections of rapidly growing size. However, as the size and complexity of these collections increase, so does the need for better methods to analyze their content. In this regard, the Sketchfab 3D Creative Commons Collection (S3D3C) introduced in this paper presents a promising solution.
The S3D3C collection consists of 40,802 creative commons licensed models that were downloaded from the popular 3D model platform, Sketchfab. Unlike previously proposed research collections, S3D3C incorporates a wide variety of technical properties found in real-world 3D models, such as textures, materials, and animations. This inclusion of popular freely available models with diverse characteristics allows for a more comprehensive evaluation of state-of-the-art geometry-based and view-based 3D model analysis and retrieval techniques.
One key aspect that sets S3D3C apart is its multi-disciplinary nature. The collection encompasses not only the field of multimedia information systems but also overlaps with concepts in animations, artificial reality, augmented reality, and virtual realities. The broad range of technical properties found in the models allows researchers from various disciplines to explore and evaluate their methods, fostering collaboration and innovation.
From a multimedia information systems perspective, the S3D3C collection provides a realistic and practical dataset for testing and benchmarking the performance of 3D model analysis algorithms. Researchers in this field can leverage this collection to improve techniques for extracting meaningful information from complex 3D models, such as object recognition, shape analysis, and semantic understanding. Moreover, the dataset’s diversity can help identify challenges and limitations faced by existing methods, pushing researchers to develop more robust and efficient solutions.
The inclusion of animation and interactive elements within the models in S3D3C opens up new possibilities for research in animations and virtual realities. Researchers can now investigate techniques for analyzing and manipulating animated 3D models, advancing fields like character animation, motion capture, and virtual reality experiences. The availability of a standardized dataset allows for fair comparisons between different approaches, fostering healthy competition and driving innovation in these areas.
The advancements in 3D model analysis and retrieval techniques facilitated by the S3D3C collection have significant implications in fields like computer graphics, computer vision, and human-computer interaction. Improved methods for analyzing and understanding 3D models can revolutionize industries ranging from gaming and entertainment to architectural design and virtual prototyping. The research conducted using this collection can pave the way for more immersive virtual experiences, better content creation tools, and more intelligent systems capable of understanding and interacting with the 3D world.
In conclusion, the introduction of the Sketchfab 3D Creative Commons Collection (S3D3C) fills an important gap in the research community when it comes to benchmarking and evaluating 3D model analysis and retrieval techniques. The multi-disciplinary nature of the collection, its inclusion of diverse technical properties, and the vast number of models make it a valuable resource for researchers from a wide range of fields. The future of 3D modeling and analysis looks promising, with the S3D3C collection serving as a catalyst for innovation and collaboration.