# dtour: A Steerable Tour de Vis Through High-Dimensional Data

arXiv: https://arxiv.org/abs/2605.04306 · HTML: https://mlsanity.com/paper/2605.04306/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2026.

## Built on by (most cited first, 1 of 1)

- [Visualizing High-Dimensional Graph Embeddings via Informed Multi-View Projections](https://mlsanity.com/paper/2606.31119/) (2026)

## Abstract (arXiv)

Understanding high-dimensional data requires projecting it into lower-dimensional spaces, but any single projection inevitably loses information or introduces distortions. Tours address this limitation through animation of 2D projection sequences, yet existing tools present tradeoffs in the freedom and steerability of projection traversal, providing little to no ability to move between expert-guided paths and unrestrained exploration. We present dtour, a tour interface that combines static projection previews, reversible scrubbing along continuous geodesic projection paths, manual projection manipulation, and a wandering grand tour, all within a single progressive exploration interface. dtour scales to millions of points via GPU-accelerated rendering, runs in any modern browser, and integrates with both Python and JavaScript ecosystems. We demonstrate dtour on text, image, and single-cell data for two usage scenarios: gradually revealing structure in high-dimensional data and validating non-linear dimensionality reduction outputs.