Vector databases have recently gained significant attention due to the emergence of large language models that produce vector embeddings for text. Existing vector databases can be broadly categorized into two types: specialized and generalized. Specialized vector databases are explicitly designed and optimized for managing vector data, while generalized ones support vector data management within a general purpose database. While specialized vector databases are interesting, there is a substantial customer base interested in generalized vector databases for various reasons, e.g., a reluctance to move data out of relational databases to reduce data silos and costs, the desire to use SQL, and the need for more sophisticated query processing of vector and non-vector data. However, generalized vector databases face two main challenges: performance and interoperability of vector search with SQL, such as combining vector search with filters, joins, or even fulltext search. In this paper, we present SingleStore-V, a full-fledged generalized vector database integrated into SingleStore, a modern distributed relational database optimized for both OLAP and OLTP workloads. SingleStore-V achieves high performance and interoperability via a suite of optimizations. Experiments on standard vector benchmarks show that SingleStore-V performs comparably to Milvus, a highly-optimized specialized vector database, and significantly outperforms pgvector, a popular generalized vector database in PostgreSQL. We believe this paper will shed light on integrating vector search into relational databases in general, as many design concepts and optimizations apply to other databases.

The last decade has seen a remarkable rise in specialized database systems. Systems for transaction processing, data warehousing, time series analysis, full-text search, data lakes, in-memory caching, document storage, queuing, graph processing, and geo-replicated operational workloads are now available to developers. A belief has taken hold that a single general-purpose database is not capable of running varied workloads at a reasonable cost with strong performance, at the level of scale and concurrency people demand today. There is value in specialization, but the complexity and cost of using multiple specialized systems in a single application environment is becoming apparent. This realization is driving developers and IT decision makers to seek databases capable of powering a broader set of use cases when looking to adopt a new database. Hybrid transaction and analytical (HTAP) databases have been developed to try to tame some of this chaos.

Stereoscopic 3D (S3D) movies have become widely popular in the movie theaters, but the adoption of S3D at home is low even though most TV sets support S3D. It is widely believed that S3D with glasses is not the right approach for the home. A much more appealing approach is to use automulti-scopic displays that provide a glasses-free 3D experience to multiple viewers. A technical challenge is the lack of native multiview content that is required to deliver a proper view of the scene for every viewpoint. Our approach takes advantage of the abundance of stereoscopic 3D movies. We propose a real-time system that can convert stereoscopic video to a high-quality multiview video that can be directly fed to automultiscopic displays. Our algorithm uses a wavelet-based decomposition of stereoscopic images with per-wavelet disparity estimation. A key to our solution lies in combining Lagrangian and Eulerian approaches for both the disparity estimation and novel view synthesis, which leverages the complementary advantages of both techniques. The solution preserves all the features of Eulerian methods, e.g., subpixel accuracy, high performance, robustness to ambiguous depth cases, and easy integration of inter-view aliasing while maintaining the advantages of Lagrangian approaches, e.g., robustness to large disparities and possibility of performing non-trivial disparity manipulations through both view extrapolation and interpolation. The method achieves real-time performance on current GPUs. Its design also enables an easy hardware implementation that is demonstrated using a field-programmable gate array. We analyze the visual quality and robustness of our technique on a number of synthetic and real-world examples. We also perform a user experiment which demonstrates benefits of the technique when compared to existing solutions.

3D printing hardware is rapidly scaling up to output continuous mixtures of multiple materials at increasing resolution over ever larger print volumes. This poses an enormous computational challenge: large high-resolution prints comprise trillions of voxels and petabytes of data and simply modeling and describing the input with spatially varying material mixtures at this scale is challenging. Existing 3D printing software is insufficient; in particular, most software is designed to support only a few million primitives, with discrete material choices per object. We present OpenFab, a programmable pipeline for synthesis of multi-material 3D printed objects that is inspired by RenderMan and modern GPU pipelines. The pipeline supports procedural evaluation of geometric detail and material composition, using shader-like fablets , allowing models to be specified easily and efficiently. We describe a streaming architecture for OpenFab; only a small fraction of the final volume is stored in memory and output is fed to the printer with little startup delay. We demonstrate it on a variety of multi-material objects.