zhangganlin/ba-t

★ 18Audience · researcherComplexity · 5/5Setup · hard

Mindmap

mindmap
  root((repo))
    What It Does
      Refines camera positions
      Aligns 3D points
      Iterative refinement
    Method
      Transformer architecture
      Two-view focus
      Bundle adjustment
    Audience
      Vision researchers
      3D reconstruction devs
    Status
      No code yet
      ArXiv paper available

mindmap root((repo)) What It Does Refines camera positions Aligns 3D points Iterative refinement Method Transformer architecture Two-view focus Bundle adjustment Audience Vision researchers 3D reconstruction devs Status No code yet ArXiv paper available

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Things people build with this

USE CASE 1

Read the arXiv paper to understand how a transformer architecture improves two-view bundle adjustment for 3D reconstruction pipelines.

USE CASE 2

Follow the repository for a future code release to experiment with camera pose refinement in your own structure-from-motion workflow.

Getting it running

Difficulty · hard Time to first run · 1day+

No code is available in the repository yet, only the accompanying arXiv paper exists.

No license information is available in this repository.

In plain English

BA-T is a research project from computer vision researchers at TU Munich, MCML, and ETH Zurich. The full name is Bundle Adjustment Transformer, and the work is described as an iterative transformer-based method for two-view bundle adjustment. Bundle adjustment is a step in 3D reconstruction pipelines: given two photos of the same scene taken from different positions, it refines the estimated camera positions and the locations of visible 3D points so that everything lines up as accurately as possible. The two-view part means the method focuses on pairs of images rather than larger sets. The transformer in the name refers to an architecture style that processes relationships between many elements at once, originally developed in language processing and since applied widely in computer vision tasks. The BA-T project applies that idea to the bundle adjustment problem in an iterative way, refining estimates over multiple passes. The project accompanies an academic paper published as a preprint on arXiv. The authors are Ganlin Zhang, Weirong Chen, Daniel Cremers, and Xi Wang, all affiliated with TU Munich and the Munich Center for Machine Learning, with one author also at ETH Zurich. At the time of this writing, the repository contains no code. The README states only that code is coming soon. No installation instructions, usage examples, or architecture details beyond the title are provided yet. Readers interested in the method should consult the arXiv paper directly.

Copy-paste prompts

Prompt 1

Explain bundle adjustment in 3D reconstruction and how applying a transformer architecture could improve it over classic optimization methods.

Prompt 2

What is two-view bundle adjustment and how does it differ from multi-view bundle adjustment in structure-from-motion pipelines?

Prompt 3

Summarize the key ideas behind iterative transformer-based camera pose refinement for the BA-T paper from TU Munich and ETH Zurich.

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