upload 3.12 legacy

Author: B1ueber2y

legacy/3.12/_images/dense.png

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+Bibliography
+============
+
+.. [schoenberger_thesis] Johannes L. Schönberger. "Robust Methods for Accurate
+   and Efficient 3D Modeling from Unstructured Imagery." ETH Zürich, 2018.
+
+.. [furukawa10] Furukawa, Yasutaka, and Jean Ponce.
+    "Accurate, dense, and robust multiview stereopsis."
+    Transactions on Pattern Analysis and Machine Intelligence, 2010.
+
+.. [hofer16] Hofer, M., Maurer, M., and Bischof, H.
+    Efficient 3D Scene Abstraction Using Line Segments,
+    Computer Vision and Image Understanding, 2016.
+
+.. [jancosek11] Jancosek, Michal, and Tomás Pajdla.
+    "Multi-view reconstruction preserving weakly-supported surfaces."
+    Conference on Computer Vision and Pattern Recognition, 2011.
+
+.. [kazhdan2013] Kazhdan, Michael and Hoppe, Hugues
+    "Screened poisson surface reconstruction."
+    ACM Transactions on Graphics (TOG), 2013.
+
+.. [schoenberger16sfm] Schönberger, Johannes Lutz and Frahm, Jan-Michael.
+    "Structure-from-Motion Revisited." Conference on Computer Vision and
+    Pattern Recognition, 2016.
+
+.. [schoenberger16mvs] Schönberger, Johannes Lutz and Zheng, Enliang and
+    Pollefeys, Marc and Frahm, Jan-Michael.
+    "Pixelwise View Selection for Unstructured Multi-View Stereo."
+    European Conference on Computer Vision, 2016.
+
+.. [schoenberger16vote] Schönberger, Johannes Lutz and Price, True and
+    Sattler, Torsten and Frahm, Jan-Michael and Pollefeys, Marc
+    "A Vote­-and­-Verify Strategy for Fast Spatial Verification in Image
+    Retrieval." Asian Conference on Computer Vision, 2016.
+
+.. [lowe04] Lowe, David G. "Distinctive image features from scale-invariant
+    keypoints". International journal of computer vision 60.2 (2004): 91-110.
+
+.. [wu13] Wu, Changchang. "Towards linear-time incremental structure from
+    motion." International Conference 3D Vision, 2013.

legacy/3.12/_images/incremental-sfm.png

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+Camera Models
+=============
+
+COLMAP implements different camera models of varying complexity. If no intrinsic
+parameters are known a priori, it is generally best to use the simplest camera
+model that is complex enough to model the distortion effects:
+
+- ``SIMPLE_PINHOLE``, ``PINHOLE``: Use these camera models, if your images are
+  undistorted a priori. These use one and two focal length parameters,
+  respectively. Note that even in the case of undistorted images, COLMAP could
+  try to improve the intrinsics with a more complex camera model.
+- ``SIMPLE_RADIAL``, ``RADIAL``: This should be the camera model of choice, if the
+  intrinsics are unknown and every image has a different camera calibration,
+  e.g., in the case of Internet photos. Both models are simplified versions of
+  the ``OPENCV`` model only modeling radial distortion effects with one and two
+  parameters, respectively.
+- ``OPENCV``, ``FULL_OPENCV``: Use these camera models, if you know the calibration
+  parameters a priori. You can also try to let COLMAP estimate the parameters,
+  if you share the intrinsics for multiple images. Note that the automatic
+  estimation of parameters will most likely fail, if every image has a separate
+  set of intrinsic parameters.
+- ``SIMPLE_RADIAL_FISHEYE``, ``RADIAL_FISHEYE``, ``OPENCV_FISHEYE``, ``FOV``,
+  ``THIN_PRISM_FISHEYE``, ``RAD_TAN_THIN_PRISM_FISHEYE``: Use these camera models
+  for fisheye lenses and note that all other models are not really capable of
+  modeling the distortion effects of fisheye lenses. The ``FOV`` model is used by
+  Google Project Tango (make sure to not initialize ``omega`` to zero).
+
+You can inspect the estimated intrinsic parameters by double-clicking specific
+images in the model viewer or by exporting the model and opening the
+``cameras.txt`` file.
+
+To achieve optimal reconstruction results, you might have to try different
+camera models for your problem. Generally, when the reconstruction fails and the
+estimated focal length values / distortion coefficients are grossly wrong, it is
+a sign of using a too complex camera model. Contrary, if COLMAP uses many
+iterative local and global bundle adjustments, it is a sign of using a too
+simple camera model that is not able to fully model the distortion effects.
+
+You can also share intrinsics between multiple
+images to obtain more reliable results
+(see :ref:`Share intrinsic camera parameters <faq-share-intrinsics>`) or you can
+fix the intrinsic parameters during the reconstruction
+(see :ref:`Fix intrinsic camera parameters <faq-fix-intrinsics>`).
+
+Please, refer to the camera models header file for information on the parameters
+of the different camera models:
+https://github.com/colmap/colmap/blob/main/src/colmap/sensor/models.h 

legacy/3.12/_images/sparse.png

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+.. _changelog:
+
+.. include:: ../CHANGELOG.rst