mlsanityData to 7 Oct 2026

LiftProj: Space Lifting and Projection-Based Panorama Stitching

arXiv 2512.24276 ↗First posted 30 Dec 2025cs.CV cs.MMcited by 1

Not on the map. Why?

Abstract (arXiv)

Traditional image stitching techniques have predominantly utilized two-dimensional homography transformations and mesh warping to achieve alignment on a planar surface. While effective for scenes that are approximately coplanar or exhibit minimal parallax, these approaches often result in ghosting, structural bending, and stretching distortions in non-overlapping regions when applied to real three-dimensional scenes characterized by multiple depth layers and occlusions. Such challenges are exacerbated in multi-view accumulations and 360deg closed-loop stitching scenarios. In response, this study introduces a spatially lifted panoramic stitching framework that initially elevates each input image into a dense three-dimensional point representation within a unified coordinate system, facilitating global cross-view fusion augmented by confidence metrics. Subsequently, a unified projection center is established in three-dimensional space, and an equidistant cylindrical projection is employed to map the fused data onto a single panoramic manifold, thereby producing a geometrically consistent 360deg panoramic layout. Finally, hole filling is conducted within the canvas domain to address unknown regions revealed by viewpoint transitions, restoring continuous texture and semantic coherence. This framework reconceptualizes stitching from a two-dimensional warping paradigm to a three-dimensional consistency paradigm and is designed to flexibly incorporate various three-dimensional lifting and completion modules. Experimental evaluations demonstrate that the proposed method substantially mitigates geometric distortions and ghosting artifacts in scenarios involving significant parallax and complex occlusions, yielding panoramic results that are more natural and consistent.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 15 of its 38 references that are in the map. First posted on arXiv 30 Dec 2025.

Builds on

15 of 38 references.

References · 15

  1. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  2. 2023DUSt3R: Geometric 3D Vision Made Easycited by 1,526
  3. 2025VGGT: Visual Geometry Grounded Transformercited by 1,475
  4. 2017Unsupervised Deep Homography: A Fast and Robust Homography Estimation Modelcited by 75
  5. 2021Unsupervised Deep Image Stitching: Reconstructing Stitched Features to Imagescited by 49
  6. 2023Parallax-Tolerant Unsupervised Deep Image Stitchingcited by 30
  7. 2018Single-Perspective Warps in Natural Image Stitchingcited by 27
  8. 2017Quasi-homography warps in image stitchingcited by 9
  9. 2024Object-level Geometric Structure Preserving for Natural Image Stitchingcited by 8
  10. 2020Image Stitching Based on Planar Region Consensuscited by 7
  11. 2017Image Stitching by Line-guided Local Warping with Global Similarity Constraintcited by 6
  12. 2022Natural Image Stitching Using Depth Mapscited by 4
3 more
  1. 2023Learning Residual Elastic Warps for Image Stitching under Dirichlet Boundary Conditioncited by 4
  2. 2023Implicit Neural Image Stitchingcited by 4
  3. 2024Parallax-tolerant Image Stitching via Segmentation-guided Multi-homography Warpingcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.