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Cited by 863

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Creating full view panoramic image mosaics and environment maps

Open access
Szeliski, Richard, Shum, Heung-Yeung·1997·Proceedings of the 24th annual conference on Computer graphics and interactive techniques - SIGGRAPH '97

Abstract

This paper presents a novel approach to creating full view panoramic mosaics from image sequences.Unlike current panoramic stitching methods, which usually require pure horizontal camera panning, our system does not require any controlled motions or constraints on how the images are taken (as long as there is no strong motion parallax).For example, images taken from a hand-held digital camera can be stitched seamlessly into panoramic mosaics.Because we represent our image mosaics using a set of transforms, there are no singularity problems such as those existing at the top and bottom of cylindrical or spherical maps.Our algorithm is fast and robust because it directly recovers 3D rotations instead of general 8 parameter planar perspective transforms.Methods to recover camera focal length are also presented.We also present an algorithm for efficiently extracting environment maps from our image mosaics.By mapping the mosaic onto an artibrary texture-mapped polyhedron surrounding the origin, we can explore the virtual environment using standard 3D graphics viewers and hardware without requiring special-purpose players.

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Metadata

Authors
Szeliski, R., & Shum, H.-Y.
Venue
Proceedings of the 24th annual conference on Computer graphics and interactive techniques - SIGGRAPH '97
Publisher
ACM Press
Published
1997
Pages
251-258
OpenAlex
W2029612144
Cited by
863
References
1
FWCI
36.58
Type
conference paper
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Creating full view panoramic image mosaics and environment maps. https://app.cochat.ai/papers/10.1145/258734.258861
The paper
Szeliski, R., & Shum, H.-Y. (1997). Creating full view panoramic image mosaics and environment maps. Proceedings of the 24th annual conference on Computer graphics and interactive techniques - SIGGRAPH '97. https://doi.org/10.1145/258734.258861

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.