This work addresses the problem of novel view synthesis in diverse scenes from small collections of RGB images. We propose ERUPT (Efficient Rendering with Unposed Patch Transformer) a state-of-the-art scene reconstruction model capable of efficient scene rendering using unposed imagery. We introduce patch-based querying, in contrast to existing pixel-based queries, to reduce the compute required to render a target view. This makes our model highly efficient both during training and at inference, capable of rendering at 600 fps on commercial hardware. Notably, our model is designed to use a learned latent camera pose which allows for training using unposed targets in datasets with sparse or inaccurate ground truth camera pose. We show that our approach can generalize on large real-world data and introduce a new benchmark dataset (MSVS-1M) for latent view synthesis using street-view imagery collected from Mapillary. In contrast to NeRF and Gaussian Splatting, which require dense imagery and precise metadata, ERUPT can render novel views of arbitrary scenes with as few as five unposed input images. ERUPT achieves better rendered image quality than current state-of-the-art methods for unposed image synthesis tasks, reduces labeled data requirements by 95% and decreases computational requirements by an order of magnitude, providing efficient novel view synthesis for diverse real-world scenes.

While biometric-face and whole-body-recognition technology have recently advanced and matured, there are increasing interests in enhanced long-range recognition capabilities. However, long-range recognition requires the use of large, specialized datasets to support research and development for next generation systems. Moreover, existing datasets are further limited by the types of modalities (face or whole-body), number of subjects, maximum standoff distance, clothing variability, or availability restrictions. For long-range recognition, low-quality probe (query) images, which are often acquired from extended standoff distances or aerial platforms, are matched against higher quality gallery images and frequently results in poor identification performance. To address the growing needs for relevant data sources, a large-scale biometric dataset was collected and curated for long-range biometric recognition. This dataset is comprised of more than 1.2 million outdoor and 250,000 indoor (face and whole-body) images from more than 250 subjects that were acquired using various high-end cameras, including Canon and Nikon DSLR cameras, surveillance cameras, specialized long-range face cameras, and UAV platforms. The primary goal of this dataset is to support the development of algorithms for face and whole-body recognition at extended standoff distances. The availability of such a dataset is crucial in advancing technology for recognition under challenging conditions such as atmospheric turbulence.