Multimodal Vision Research Laboratory

MVRL

Research Area: Geometric Vision

We develop methods for geometric understanding and 3D scene reconstruction from images. Recent work includes open-world generation of stereo images with unsupervised matching (GenStereo), consistent text-to-360 scene generation (PanoDreamer), generative-free 3D scene recovery for occlusion removal (DeclutterNeRF), and omnidirectional depth estimation via stereo matching from multi-cylindrical panoramas (MCPDepth). We also work on detecting vanishing points using global image context, learning to look around objects for top-view representations of outdoor scenes, and geometric calibration methods using natural cues like rainbows and horizon lines.

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Publications

  1. Thumbnail for Towards Open-World Generation of Stereo Images and Unsupervised Matching
    Qiao F, Xiong Z, Xing E, Jacobs N. 2025. Towards Open-World Generation of Stereo Images and Unsupervised Matching. In: IEEE International Conference on Computer Vision (ICCV).
    code website paper bibtex GenAI Geometry
  2. Xiong Z, Chen Z, Li Z, Xu Y, Jacobs N. 2025. PanoDreamer: Consistent Text to 360 Scene Generation. In: 4th Computer Vision for Metaverse Workshop (IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops).
    paper bibtex GenAI Geometry
  3. Liu W, Xiong Z, Li X, Jacobs N. 2025. DeclutterNeRF: Generative-Free 3D Scene Recovery for Occlusion Removal. In: 4th Computer Vision for Metaverse Workshop (IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops).
    paper bibtex GenAI Geometry
  4. Thumbnail for MCPDepth: Omnidirectional Depth Estimation via Stereo Matching from Multi-Cylindrical Panoramas
    Qiao F, Xiong Z, Zhu X, Ma Y, He Q, Jacobs N. 2024. MCPDepth: Omnidirectional Depth Estimation via Stereo Matching from Multi-Cylindrical Panoramas.
    paper bibtex Transportation Geometry
  5. Thumbnail for Detecting Vanishing Points using Global Image Context in a Non-Manhattan World
    Zhai M, Workman S, Jacobs N. 2016. Detecting Vanishing Points using Global Image Context in a Non-Manhattan World. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). DOI: 10.1109/CVPR.2016.610.
    code paper doi bibtex Calibration Geometry
  6. Thumbnail for Scene Shape Estimation from Multiple Partly Cloudy Days
    Workman S, Souvenir R, Jacobs N. 2015. Scene Shape Estimation from Multiple Partly Cloudy Days. Computer Vision and Image Understanding (CVIU):116–129. DOI: 10.1016/j.cviu.2014.10.002.
    website doi bibtex Calibration Geometry
  7. Workman S, Greenwell C, Zhai M, Baltenberger R, Jacobs N. 2015. DeepFocal: A Method for Direct Focal Length Estimation. In: IEEE International Conference on Image Processing (ICIP). DOI: 10.1109/ICIP.2015.7351024.
    website paper doi bibtex Calibration Social Media Geometry
  8. Thumbnail for A Pot of Gold: Rainbows as a Calibration Cue
    Workman S, Mihail RP, Jacobs N. 2014. A Pot of Gold: Rainbows as a Calibration Cue. In: European Conference on Computer Vision (ECCV). 820–835. DOI: 10.1007/978-3-319-10602-1_53.
    website paper doi bibtex Calibration Geometry