The vitrivr multimedia retrieval stack has been shown to be versatile and effective in many areas and application domains. In this paper, we present vitrivr's new retrieval engine, the vitrivr-engine, a flexible and highly configurable information extraction and query processing solution with support for a broad range of media types.

Within the domain of multimedia formats, 3D models – alongside images, videos, and texts – are rapidly gaining prominence in applications and research. As the uses of 3D models increase and it becomes easier and more accessible to capture and digitally create 3D models, methods are required to allow automatic analysis and retrieval within large collections. While a lot of previous work has focused on geometry-based retrieval, these methods usually require an example 3D model to query, and often struggle to capture appearance information expressed through textures and materials.In this paper, we propose a novel view-based approach that enables embedding of 3D models into multi-modal embedding spaces, by rendering 2D planar projections. Furthermore, our approach addresses the challenge of viewpoint selection for 3D model rendering, in order to maximize its semantic recognition. We demonstrate the capability of our approach using two pre-trained multi-modal embedding models applied to a large collection of modern 3D models.

Video retrieval still offers many challenges which can so far only be effectively mediated through interactive, human-in-the-loop retrieval approaches. The vitrivr multimedia retrieval stack offers a broad range of query mechanisms to enable users to perform such interactive retrieval. While these multiple mechanisms offer various options to experienced users, they can be difficult to use for novices. In this paper, we present a minimal user interface geared towards novice users that only exposes a subset of vitrivr’s functionality but simplifies user interaction.