𝗨𝗻𝗶𝗳𝗶𝗲𝗱 𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿 𝗳𝗼𝗿 𝟯𝗗 𝗦𝗰𝗲𝗻𝗲 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 Sebastian Koch, Johanna Wald, Hide Matsuki ... Federico Tombari arxiv.org/abs/2512.14364 Trending on www.scholar-inbox.com

Full draft of the SLAM Handbook now released --- available as a free PDF, with a printed version coming soon. Now including Part 3, "From SLAM to Spatial AI" (I knew it would catch on eventually), with contributions from @hidematsu82.bsky.social and me. #SpatialAI github.com/SLAM-Handboo...

For more please visit: Website: edexheim.github.io/mast3r-slam/ Video: youtu.be/wozt71NBFTQ

For robustness, MASt3R-SLAM performs relocalisation allowing it to handle the kidnapped robot problem. As a purely monocular SLAM, it loses track when the camera’s view is obstructed, but as soon as the view is unblocked, it immediately relocalises and resumes mapping.

Introducing MASt3R-SLAM, the first real-time monocular dense SLAM with MASt3R as a foundation. Easy to use like DUSt3R/MASt3R, from an uncalibrated RGB video it recovers accurate, globally consistent poses & a dense map. With @ericdexheimer.bsky.social* @ajdavison.bsky.social (*Equal Contribution)

MASt3R-SLAM: easily the most robust dense monocular SLAM system I've ever seen, and also very accurate. Real-time, RGB only (no IMU), handles unknown focal length and zoom. More new videos coming soon @ericdexheimer.bsky.social @rmurai0610.bsky.social. Paper and more: edexheim.github.io/mast3r-slam

Nice question! My favorite scene representation is code -- procedurally generated scenes. One of my most beloved videos is Inigo Quilez's "Painting a Landscape with Maths" (www.youtube.com/watch?v=BFld...). This nicely shows the power of composing simple functions in synthesizing visual complexity.