convert a 2d drawing to 3d

Procedure of transforming 2nd film to 3D form

2D to 3D conversion

Process blazon	digital and impress
Industrial sector(s)	Motion picture and television, print production
Main technologies or sub-processes	Figurer software
Product(southward)	Movies, television shows, social media, printed images

2D to 3D video conversion (too called 2D to stereo 3D conversion and stereo conversion) is the process of transforming second ("flat") picture to 3D grade, which in virtually all cases is stereo, and so information technology is the process of creating imagery for each eye from i 2D image.

Overview [edit]

2D-to-3D conversion adds the binocular disparity depth cue to digital images perceived by the brain, thus, if done properly, greatly improving the immersive consequence while viewing stereo video in comparison to 2D video. However, in society to exist successful, the conversion should be washed with sufficient accuracy and correctness: the quality of the original 2D images should not deteriorate, and the introduced disparity cue should not contradict other cues used past the brain for depth perception. If washed properly and thoroughly, the conversion produces stereo video of similar quality to "native" stereo video which is shot in stereo and accurately adjusted and aligned in post-product.^[1]

Ii approaches to stereo conversion can be loosely defined: quality semiautomatic conversion for movie theatre and high quality 3DTV, and low-quality automatic conversion for cheap 3DTV, VOD and like applications.

Re-rendering of estimator blithe films [edit]

Calculator animated 2D films fabricated with 3D models tin be re-rendered in stereoscopic 3D past adding a second virtual photographic camera if the original data is still available. This is technically non a conversion; therefore, such re-rendered films have the same quality every bit films originally produced in stereoscopic 3D. Examples of this technique include the re-release of Toy Story and Toy Story ii. Revisiting the original computer information for the two films took four months, too as an additional half dozen months to add the 3D.^[2] However, not all CGI films are re-rendered for the 3D re-release because of the costs, time required, lack of skilled resources or missing computer data.

Importance and applicability [edit]

With the increase of films released in 3D, second to 3D conversion has become more than common. The majority of non-CGI stereo 3D blockbusters are converted fully or at least partially from 2D footage. Even Avatar contains several scenes shot in 2D and converted to stereo in post-product.^[3] The reasons for shooting in second instead of stereo are financial, technical and sometimes artistic:^{[i] [iv]}

• Stereo post-production workflow is much more than complex and not equally well-established as second workflow, requiring more work and rendering.
• Professional stereoscopic rigs are much more expensive and bulky than customary monocular cameras. Some shots, peculiarly activeness scenes, can be simply shot with relatively small 2D cameras.
• Stereo cameras tin innovate various mismatches in stereo image (such as vertical parallax, tilt, colour shift, reflections and glares in different positions) that should be stock-still in post-product anyway because they ruin the 3D effect. This correction sometimes may accept complexity comparable to stereo conversion.
• Stereo cameras can betray practical effects used during filming. For example, some scenes in the Lord of the Rings film trilogy were filmed using forced perspective to permit 2 actors to appear to be unlike concrete sizes. The same scene filmed in stereo would reveal that the actors were not the same altitude from the camera.
• By their very nature, stereo cameras have restrictions on how far the photographic camera can be from the filmed subject and still provide acceptable stereo separation. For example, the simplest way to film a scene set on the side of a building might be to employ a camera rig from across the street on a neighboring edifice, using a zoom lens. However, while the zoom lens would provide acceptable image quality, the stereo separation would be almost zip over such a distance.

Even in the case of stereo shooting, conversion can frequently be necessary. Also the mentioned difficult-to-shoot scenes, there are situations when mismatches in stereo views are too big to accommodate, and it is simpler to perform 2nd to stereo conversion, treating 1 of the views as the original 2D source.

Full general issues [edit]

Without respect to detail algorithms, all conversion workflows should solve the following tasks:^{[4] [five]}

1. Allocation of "depth budget" – defining the range of permitted disparity or depth, what depth value corresponds to the screen position (so-chosen "convergence indicate" position), the permitted distance ranges for out-of-the-screen effects and behind-the-screen background objects. If an object in stereo pair is in exactly the aforementioned spot for both eyes, and then it volition appear on the screen surface and information technology volition exist in nothing parallax. Objects in forepart of the screen are said to be in negative parallax, and background imagery behind the screen is in positive parallax. There are the corresponding negative or positive offsets in object positions for left and correct heart images.
2. Control of comfy disparity depending on scene blazon and motility – besides much parallax or alien depth cues may cause eye-strain and nausea effects
3. Filling of uncovered areas – left or right view images testify a scene from a different angle, and parts of objects or entire objects covered by the foreground in the original 2D prototype should become visible in a stereo pair. Sometimes the background surfaces are known or can be estimated, so they should exist used for filling uncovered areas. Otherwise the unknown areas must be filled in past an artist or inpainted, since the verbal reconstruction is non possible.

Loftier quality conversion methods should as well deal with many typical problems including:

• Translucent objects
• Reflections
• Fuzzy semitransparent object borders – such as hair, fur, foreground out-of-focus objects, thin objects
• Moving-picture show grain (existent or artificial) and similar racket effects
• Scenes with fast erratic motion
• Small particles – rain, snow, explosions and so on.

Quality semiautomatic conversion [edit]

Depth-based conversion [edit]

Nearly semiautomatic methods of stereo conversion employ depth maps and depth-paradigm-based rendering.^{[4] [v]}

The thought is that a separate auxiliary moving-picture show known as the "depth map" is created for each frame or for a series of homogenous frames to indicate depths of objects present in the scene. The depth map is a separate grayscale image having the same dimensions as the original 2D image, with diverse shades of greyness to betoken the depth of every part of the frame. While depth mapping can produce a fairly potent illusion of 3D objects in the video, it inherently does not support semi-transparent objects or areas, nor does it stand for occluded surfaces; to emphasize this limitation, depth-based 3D representations are ofttimes explicitly referred to as 2.5D.^{[vi] [7]} These and other similar issues should be dealt with via a separate method. ^{[6] [viii] [9]}

Generating and reconstructing 3D shapes from unmarried or multi-view depth maps or silhouettes ^[10]

The major steps of depth-based conversion methods are:

1. Depth budget allocation – how much total depth in the scene and where the screen airplane will be.
2. Image segmentation, creation of mattes or masks, usually past rotoscoping. Each important surface should be isolated. The level of detail depends on the required conversion quality and budget.
3. Depth map creation. Each isolated surface should be assigned a depth map. The separate depth maps should be composed into a scene depth map. This is an iterative process requiring adjustment of objects, shapes, depth, and visualization of intermediate results in stereo. Depth micro-relief, 3D shape is added to most important surfaces to forestall the "cardboard" effect when stereo imagery looks like a combination of flat images just set at different depths.
4. Stereo generation based on 2nd+Depth with any supplemental information similar clean plates, restored background, transparency maps, etc. When the process is complete, a left and right paradigm volition have been created. Usually the original 2D prototype is treated every bit the heart paradigm, so that 2 stereo views are generated. Even so, some methods suggest to use the original image equally i center's epitome and to generate merely the other middle'south epitome to minimize the conversion cost.^[4] During stereo generation, pixels of the original epitome are shifted to the left or to the right depending on depth map, maximum selected parallax, and screen surface position.
5. Reconstruction and painting of whatsoever uncovered areas not filled by the stereo generator.

Stereo can be presented in any format for preview purposes, including anaglyph.

Time-consuming steps are image sectionalisation/rotoscoping, depth map creation and uncovered expanse filling. The latter is especially important for the highest quality conversion.

In that location are various automation techniques for depth map creation and groundwork reconstruction. For instance, automatic depth interpretation can exist used to generate initial depth maps for certain frames and shots.^[11]

People engaged in such work may exist called depth artists.^[12]

Multi-layering [edit]

A development on depth mapping, multi-layering works effectually the limitations of depth mapping past introducing several layers of grayscale depth masks to implement limited semi-transparency. Like to a simple technique,^[13] multi-layering involves applying a depth map to more than one "piece" of the flat image, resulting in a much better approximation of depth and protrusion. The more layers are processed separately per frame, the college the quality of 3D illusion tends to be.

Other approaches [edit]

3D reconstruction and re-projection may exist used for stereo conversion. It involves scene 3D model creation, extraction of original paradigm surfaces equally textures for 3D objects and, finally, rendering the 3D scene from two virtual cameras to acquire stereo video. The approach works well enough in example of scenes with static rigid objects like urban shots with buildings, interior shots, but has problems with non-rigid bodies and soft fuzzy edges.^[3]

Another method is to prepare both left and right virtual cameras, both kickoff from the original photographic camera simply splitting the commencement difference, and then painting out occlusion edges of isolated objects and characters. Essentially clean-plating several background, mid footing and foreground elements.

Binocular disparity can likewise be derived from simple geometry.^[xiv]

Automated conversion [edit]

Depth from motility [edit]

It is possible to automatically approximate depth using unlike types of motion. In case of photographic camera motion, a depth map of the entire scene tin be calculated. Also, object motion can be detected and moving areas can be assigned with smaller depth values than the groundwork. Occlusions provide information on relative position of moving surfaces.^{[15] [xvi]}

Depth from focus [edit]

Approaches of this type are also called "depth from defocus" and "depth from mistiness".^{[fifteen] [17]} On "depth from defocus" (DFD) approaches, the depth information is estimated based on the corporeality of blur of the considered object, whereas "depth from focus" (DFF) approaches tend to compare the sharpness of an object over a range of images taken with different focus distances in order to find out its distance to the camera. DFD just needs two or three at different focus to properly work, whereas DFF needs x to 15 images at least but is more authentic than the previous method.

If the heaven is detected in the candy image, it tin can also be taken into account that more than afar objects, also being hazy, should be more desaturated and more than blueish because of a thick air layer.^[17]

Depth from perspective [edit]

The idea of the method is based on the fact that parallel lines, such as railroad tracks and roadsides, appear to converge with distance, eventually reaching a vanishing point at the horizon. Finding this vanishing point gives the uttermost bespeak of the whole paradigm.^{[15] [17]}

The more the lines converge, the further away they appear to exist. So, for depth map, the area between two neighboring vanishing lines can exist approximated with a gradient plane.

Conversion artifacts [edit]

• Cardboard effect is a miracle in which 3D objects located at different depths announced flat to the audience, as if they were fabricated of cardboard, while the relative depth between the objects is preserved
• Edge sharpness mismatch - this artifact may appear due to a blurred depth map at the boundaries of objects. The edge becomes precise in one view and blurred in another. The edge-sharpness mismatch artifact is typically caused past the following:
  • Use of a "rubber sheet" technique, defined as warping the pixels surrounding the occlusion regions to avoid explicit occlusion filling. In such cases, the edges of the displacement map are blurred and the transition between foreground and background regions is smoothed. The region occupied past edge/motion blur is either "stretched" or "tucked," depending on the direction of object displacement. Naturally, this approach leads to mismatches in edge sharpness between the views.
  • Lack of proper handling of semitransparent edges, potentially resulting in border doubling or ghosting.
  • Simple apoplexy-filling techniques leading to stretching artifacts nearly object edges.

• Stuck to background objects - this error of "sticking" foreground objects to the background

3D quality metrics [edit]

PQM [edit]

PQM^[18] mimic the HVS as the results obtained aligns very closely to the Mean Opinion Score (MOS) obtained from subjective tests. The PQM quantifies the baloney in the luminance, and contrast distortion using an approximation (variances) weighted by the hateful of each pixel block to obtain the distortion in an prototype. This distortion is subtracted from 1 to obtain the objective quality score.

HV3D [edit]

HV3D^[xix] quality metric has been designed having the human visual 3D perception in heed. It takes into account the quality of the individual correct and left views, the quality of the cyclopean view (the fusion of the correct and left view, what the viewer perceives), besides every bit the quality of the depth information.

VQMT3D [edit]

The VQMT3D project ^[xx] includes several adult metrics for evaluating the quality of 2D to 3D conversion

Metric	Grade	Type	Applicable to
Paper-thin result	Avant-garde	Qualitative	2D-to-3D conversion
Border-sharpness mismatch	Unique	Qualitative	2nd-to-3D conversion
Stuck-to-background objects	Unique	Qualitative	2nd-to-3D conversion
Comparing with the 2D version	Unique	Qualitative	2D-to-3D conversion

Run across also [edit]

• Autostereoscopy
• Crosstalk (electronics)
• Digital 3D
• Picture show colorization – many of the issues involved in 3D conversion, such as object edge identification/recognition, are too encountered in colorization
• Legend3D
• List of 3D films
• Stereoscopic video game – many Southward-3D video games do not actually return two images but employ 2nd + depth rendering conversion techniques likewise
• Construction from motion
• 2d-plus-depth
• 3D display
• 3D moving-picture show
• 3D reconstruction from multiple images

References [edit]

1. ^ ^{a b} Barry Sandrew. "2D – 3D Conversion Can Exist Ameliorate Than Native 3D"
2. ^ Spud, Mekado (Oct 1, 2009). "Buzz and Woody Add a Dimension". The New York Times . Retrieved February 18, 2010.
3. ^ ^{a b} Mike Seymour. Fine art of Stereo conversion: second to 3D
4. ^ ^{a b c d} Scott Squires. 2D to 3D Conversions
5. ^ ^{a b} Jon Karafin. State-of-the-Art 2D to 3D Conversion and Stereo VFX Archived 2012-04-26 at the Wayback Auto International 3D Society University. Presentation from the October 21, 2011 3DU-Japan consequence in Tokyo.
6. ^ ^{a b} Wu, Jiajun; et al. (2017). MarrNet: 3D Shape Reconstruction via ii.5D Sketches (PDF). Conference on Neural Information Processing Systems (NeurIPS). pp. 540–550.
7. ^ Tateno, Keisuke; et al. (2016). When 2.5D is not enough: Simultaneous reconstruction, segmentation and recognition on dumbo SLAM (PDF). IEEE International Conference on Robotics and Automation (ICRA). pp. 2295–2302.
8. ^ Rock, Jason; et al. (2015). Completing 3D Object Shape from One Depth Image (PDF). IEEE Briefing on Computer Vision and Blueprint Recognition (CVPR). pp. 2484–2493.
9. ^ Shin, Daeyun; et al. (2019). 3D Scene Reconstruction with Multi-layer Depth and Epipolar Transformers (PDF). IEEE International Conference on Computer Vision (ICCV). pp. 2172–2182.
10. ^ "Soltani, A. A., Huang, H., Wu, J., Kulkarni, T. D., & Tenenbaum, J. B. Synthesizing 3D Shapes via Modeling Multi-View Depth Maps and Silhouettes With Deep Generative Networks. In Proceedings of the IEEE Briefing on Computer Vision and Pattern Recognition (pp. 1511-1519)". GitHub. 2019-07-11.
11. ^ YUVsoft. 2D–to–Stereo 3D Conversion Procedure
12. ^ Mike Eisenberg (31 October 2011). "Interview with 3D Artist Adam Hlavac". Screen Rant . Retrieved 28 December 2015.
13. ^ Cutler, James. "Masking Multiple Layers in Adobe Photoshop". Archived from the original on January 18, 2012.
14. ^ Converting a 2D picture to a 3D Lenticular Print
15. ^ ^{a b c} Dr. Lai-Homo Po. Automatic 2nd-to-3D Video Conversion Techniques for 3DTV Department of Electronic Applied science, City Academy of Hong Kong. 13 April 2010
16. ^ Automatic second to 2d-plus-Depth conversion sample for a camera motion scene
17. ^ ^{a b c} Qingqing We. "Converting 2nd to 3D: A Survey" (PDF). Faculty of Electrical Engineering, Mathematics and Informatics, Delft University of Technology. Archived from the original (PDF) on 2012-04-15.
18. ^ Joveluro, P.; Malekmohamadi, H.; Fernando, W. A. C; Kondoz, A. M. (2010). Perceptual Video Quality Metric for 3D video quality cess. IEEE. doi:ten.1109/3dtv.2010.5506331.
19. ^ Banitalebi-Dehkordi, Amin; Pourazad, Mahsa T.; Nasiopoulos, Panos (2013). 3D video quality metric for 3D video compression. IEEE. arXiv:1803.04629. doi:10.1109/ivmspw.2013.6611930.
20. ^ VQMT3D

• Mansi Sharma; Santanu Chaudhury; Brejesh Lall (2014). Kinect-Diversity Fusion: A Novel Hybrid Approach for Artifacts-Free 3DTV Content Generation. In 22nd International Conference on Pattern Recognition (ICPR), Stockholm, 2014. doi:10.1109/ICPR.2014.395.

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Source: https://en.wikipedia.org/wiki/2D_to_3D_conversion

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