Eric Brachmann
@ericbrachmann.bsky.social
Niantic Spatial, Research.
Throws machine learning at traditional computer vision pipelines to see what sticks. Differentiates the non-differentiable.
📍Europe 🔗 http://ebrach.github.io
Throws machine learning at traditional computer vision pipelines to see what sticks. Differentiates the non-differentiable.
📍Europe 🔗 http://ebrach.github.io
‼️ Major update to the #ACEZero code base. We added the capabilities of the #ICCV2025 paper on Scene Coordinate Reconstruction Priors.
Gives you, e.g., a RGB-D version of ACE0. In the snipped below, I jointly reconstruct multiple RGB-D scans of ScanNet.
github.com/nianticlabs/...
Gives you, e.g., a RGB-D version of ACE0. In the snipped below, I jointly reconstruct multiple RGB-D scans of ScanNet.
github.com/nianticlabs/...
November 10, 2025 at 9:49 AM
‼️ Major update to the #ACEZero code base. We added the capabilities of the #ICCV2025 paper on Scene Coordinate Reconstruction Priors.
Gives you, e.g., a RGB-D version of ACE0. In the snipped below, I jointly reconstruct multiple RGB-D scans of ScanNet.
github.com/nianticlabs/...
Gives you, e.g., a RGB-D version of ACE0. In the snipped below, I jointly reconstruct multiple RGB-D scans of ScanNet.
github.com/nianticlabs/...
🌟 Scene Coordinate Reconstruction Priors at #ICCV2025 🌟
Can we learn what a successful reconstruction looks like, and use this knowledge when reconstructing new scenes?
Explainer: youtu.be/RkV6U5xYc20
Project Page: nianticspatial.github.io/scr-priors
Inspiring work by Wenjing Bian et al.!
Can we learn what a successful reconstruction looks like, and use this knowledge when reconstructing new scenes?
Explainer: youtu.be/RkV6U5xYc20
Project Page: nianticspatial.github.io/scr-priors
Inspiring work by Wenjing Bian et al.!
October 15, 2025 at 7:36 AM
🌟 Scene Coordinate Reconstruction Priors at #ICCV2025 🌟
Can we learn what a successful reconstruction looks like, and use this knowledge when reconstructing new scenes?
Explainer: youtu.be/RkV6U5xYc20
Project Page: nianticspatial.github.io/scr-priors
Inspiring work by Wenjing Bian et al.!
Can we learn what a successful reconstruction looks like, and use this knowledge when reconstructing new scenes?
Explainer: youtu.be/RkV6U5xYc20
Project Page: nianticspatial.github.io/scr-priors
Inspiring work by Wenjing Bian et al.!
🔥 ACE-G, the next evolutionary step of ACE at #ICCV2025 🔥
We disentangle coordinate regression and latent map representation which lets us pre-train the regressor to generalize from mapping data to difficult query images.
Page: nianticspatial.github.io/ace-g/
Stellar work by Leonard Bruns et al.!
We disentangle coordinate regression and latent map representation which lets us pre-train the regressor to generalize from mapping data to difficult query images.
Page: nianticspatial.github.io/ace-g/
Stellar work by Leonard Bruns et al.!
October 14, 2025 at 8:24 AM
🔥 ACE-G, the next evolutionary step of ACE at #ICCV2025 🔥
We disentangle coordinate regression and latent map representation which lets us pre-train the regressor to generalize from mapping data to difficult query images.
Page: nianticspatial.github.io/ace-g/
Stellar work by Leonard Bruns et al.!
We disentangle coordinate regression and latent map representation which lets us pre-train the regressor to generalize from mapping data to difficult query images.
Page: nianticspatial.github.io/ace-g/
Stellar work by Leonard Bruns et al.!
FastForward was trained on challenging frame sets. Therefore, it is able to solve difficult relocalization cases, like opposing shots in the video below. We only have mapping images of the front of the sign. Still FF relocalizes well even behind the sign.
October 2, 2025 at 8:48 AM
FastForward was trained on challenging frame sets. Therefore, it is able to solve difficult relocalization cases, like opposing shots in the video below. We only have mapping images of the front of the sign. Still FF relocalizes well even behind the sign.
Building visual maps via reconstruction (SfM, SCR) can take time. Instead:
⏩ Get poses for your mapping images, e.g. via SLAM in real time.
⏩ Build a retrieval index in seconds.
⏩ FastForward predicts the relative pose between a query image and all retrieved images in one forward pass.
⏩ Get poses for your mapping images, e.g. via SLAM in real time.
⏩ Build a retrieval index in seconds.
⏩ FastForward predicts the relative pose between a query image and all retrieved images in one forward pass.
October 2, 2025 at 8:48 AM
Building visual maps via reconstruction (SfM, SCR) can take time. Instead:
⏩ Get poses for your mapping images, e.g. via SLAM in real time.
⏩ Build a retrieval index in seconds.
⏩ FastForward predicts the relative pose between a query image and all retrieved images in one forward pass.
⏩ Get poses for your mapping images, e.g. via SLAM in real time.
⏩ Build a retrieval index in seconds.
⏩ FastForward predicts the relative pose between a query image and all retrieved images in one forward pass.
⏩ FastForward ⏩ A new model for efficient visual relocalization. Accurate camera poses without building structured 3D maps.
nianticspatial.github.io/fastforward/
Work by @axelbarroso.bsky.social, Tommaso Cavallari and Victor Adrian Prisacariu.
nianticspatial.github.io/fastforward/
Work by @axelbarroso.bsky.social, Tommaso Cavallari and Victor Adrian Prisacariu.
October 2, 2025 at 8:48 AM
⏩ FastForward ⏩ A new model for efficient visual relocalization. Accurate camera poses without building structured 3D maps.
nianticspatial.github.io/fastforward/
Work by @axelbarroso.bsky.social, Tommaso Cavallari and Victor Adrian Prisacariu.
nianticspatial.github.io/fastforward/
Work by @axelbarroso.bsky.social, Tommaso Cavallari and Victor Adrian Prisacariu.