Falconn++ can filter out potential far away points in any hash bucket before querying, which results in higher quality candidates compared to other hashing-based solutions. Theoretically, Falconn++ asymptotically achieves lower query time complexity than Falconn, an optimal locality-sensitive hashing scheme on angular distance. Empirically, Falconn++ achieves a higher recall-speed tradeoff than Falconn on many real-world data sets. Falconn++ is also competitive with HNSW, an efficient representative of graph-based solutions on high search recall regimes.

Nevertheless, the following questions still remain very relevant: 1. Large LRs are preferred but how large are we talking about? 2. What are the key characteristics of the models trained with different LRs?

Vectors in real coordinate space are denoted in boldface with lowercase letters, e.g.

A data-driven approach, however, could overcome limitations in such rule-based approaches and improve precision mappings for individuals.

Falconn++ is also competitive with HNSW, an efficient representative of graph-based solutions on high search recall regimes.

Accurate 6D pose estimation has gained more attention over the years for robotics-assisted tasks that require precise interaction with physical objects. This paper presents an interactive 3D-to-2D visualization and annotation tool to support the 6D pose estimation research community. To the best of our knowledge, the proposed work is the first tool that allows users to visualize and manipulate 3D objects interactively on a 2D real-world scene, along with a comprehensive user study. This system supports robust 6D camera pose annotation by providing both visual cues and spatial relationships to determine object position and orientation in various environments. The annotation feature in Vision6D is particularly helpful in scenarios where the transformation matrix between the camera and world objects is unknown, as it enables accurate annotation of these objects' poses using only the camera intrinsic matrix. This capability serves as a foundational step in developing and training advanced pose estimation models across various domains. We evaluate Vision6D's effectiveness by utilizing widely-used open-source pose estimation datasets Linemod and HANDAL through comparisons between the default ground-truth camera poses with manual annotations. A user study was performed to show that Vision6D generates accurate pose annotations via visual cues in an intuitive 3D user interface. This approach aims to bridge the gap between 2D scene projections and 3D scenes, offering an effective way for researchers and developers to solve 6D pose annotation related problems. The software is open-source and publicly available at https://github.com/InteractiveGL/vision6D.

Falconn can filter out potential far away points in any hash bucket before querying, which results in higher quality candidates compared to other hashing-based solutions. Theoretically, Falconn asymptotically achieves lower query time complexity than Falconn, an optimal locality-sensitive hashing scheme on angular distance. Empirically, Falconn achieves a higher recall-speed tradeoff than Falconn on many real-world data sets. Falconn is also competitive with HNSW, an efficient representative of graph-based solutions on high search recall regimes.