CN103700138A - Sample data-based dynamic water surface reestablishing method - Google Patents
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Abstract
The invention relates to a sample data-based dynamic water surface reestablishing method, and belongs to the technical field of virtual reality. The reestablishing method comprises the following steps: (1) establishing a multi-camera dynamic water surface data collection device, calibrating cameras, and collecting movement scene images of the dynamic water surface which is required to be reestablished; (2) extracting angle points from the images, determining the mapping relation between each angle point of the images and a water bottom point, and interpolating to obtain a pixel-to-pixel mapping relation; (3) grouping a plurality of cameras in pairs, reestablishing a water surface height field by adopting a voxel subdivision rejection method by each group of cameras based on water surface refractive properties; (4) performing weight fusion among a plurality of groups of water surface height fields at the same moment so as to reduce the reestablishing errors caused by image noises of different cameras; (5) further applying the result obtained by the fusion of a plurality of groups of water surface height fields to drive a physics-based shallow water equation simulation process to generate a reestablishing result height field which has real data support and abundant detail effects.
Description
Technical field
The invention belongs to computer virtual reality technology field, be specifically related to a kind of dynamic water surface method for reconstructing based on sampled data.
Background technology
In computer graphics and vision field, people attempt to utilize computer vision reappear theory real world around always.Liquid, particularly water, as a kind of basic native element, are materials common in actual life.In computing machine, modeling generates liquid model true to nature has very high using value at aspects such as film special efficacy, Entertainment, simulation training, the condition of a disaster previews.
Fluid simulation research based on physics is a focus in computer graphics always.These class methods be take Fluid Mechanics Computation (CFD) as basis, and the mode solving by computer numerical solves the physical equation of describing fluid motion, obtain each motion state of fluid constantly.Through the development in twenty or thirty year, the fluid simulation method based on physics can be simulated the fluid motion of more complicated, and analog result details is abundant, and effect is also more and more true to nature.But these class methods also exist many shortcomings, such as computation complexity is high, numerical error accumulation etc., and the physical equation that solves of the method can not be described all motion morphologies of liquid in real world completely.
Three-dimensional body or the phenomenon of rebuilding in real world are very important research directions in computer vision.Someone begins one's study and uses the method for reconstructing based on image to carry out modeling to liquid in recent years.These class methods are caught the image of liquid motion conventionally at diverse location with a plurality of cameras, then use the modeling method based on image to obtain from image reconstruction the liquid three-dimensional model that each frame is corresponding.But liquid belongs to Non Lambert reflector, can there is the phenomenons such as high light reflectivity, refraction on surface in light, and become violent during liquid itself, so the reconstruction of liquid is a very challenging job.These class methods have had some achievements at present, can rebuild the mild liquid scene of motion.These class methods data from real world, so reconstructed results is truer.Conventionally the refraction attribute based on liquid, rebuilds from image angle, each constantly a plurality of cameras jointly participate in rebuilding and obtain a model and directly export.
Based on above-mentioned background, the present invention proposes a kind of dynamic water surface method for reconstructing based on sampled data.A plurality of cameras are rebuild after being divided into many groups between two, rebuild between the water surface elevation field obtaining and are weighted fusion, the reconstruction error bringing to reduce different cameral picture noise.Merge the water surface elevation field obtaining and be further used for driving the shallow water equation simulation process based on physics, with the advantage in conjunction with both, generate and have the final reconstructed results height field that True Data supports and details is abundant.In addition, the method for every group of camera reconstruction is from voxel angle and non-image, and counting yield is higher.
Summary of the invention
The object of the invention is: overcome based on image rebuilding method and some limitation based on physical simulating method, propose a kind of dynamic water surface method for reconstructing based on sampled data, can generate True Data and support and the abundant water surface model of details.
For completing object of the present invention, the technical solution used in the present invention is: a kind of dynamic water surface method for reconstructing based on sampled data, use a plurality of collected by camera dynamic water surface data, camera divides into groups between two, every group of camera rebuild water surface elevation field by the method that voxel segmentation is rejected, between a plurality of water surface elevations field obtaining, be weighted fusion, obtain the water surface and merge height field, the water surface merges height field and further drives the shallow water equation simulation process based on physics, and generating existing True Data support has again the reconstructed results height field that enriches details effect; It is characterized in that comprising the steps:
(1) build the dynamic water surface data collector of polyphaser, the black and white gridiron pattern plate of known dimensions is close to the bottom, a plurality of cameras are placed in water surface top different visual angles around, fixed camera position guarantees that each camera can photograph complete water-bed cross-hatch pattern picture, and between a plurality of cameras, guaranteeing can synchronous acquisition image.A plurality of cameras are carried out to inside and outside ginseng to be demarcated.Manufacture the water surface moving scene that needs collection, polyphaser high-speed synchronous gathers water-bed tessellated image under water surface motion conditions;
(2) initial time image detects X-comers by Harris Corner Detection Algorithm, and follow-up time chart image angle point is followed the trail of and determined by Lucas-Kanade optical flow method; Utilize the inside and outside ginseng of camera, determine the mapping relations of each camera image angle point and water-bed X-comers; And to the mapping relations interpolation between image angle point, obtain the mapping relations by pixel;
(3) camera is divided into a plurality of groups between two, the image pixel of trying to achieve based on step (2) and the mapping relations of water-bed X-comers, the method that each group utilizes voxel segmentation to reject is rebuild and is obtained a plurality of water surface elevations field;
(4) for each sampling instant of step (1) camera, utilize step (3) the water surface elevation field that camera reconstruction does not obtain on the same group to carry out re-projection and calculate re-projection error, a plurality of water surface elevations field is weighted to fusion, the water surface elevation field weight that re-projection error is little is larger, obtains the water surface and merges height field;
(5) water surface that utilizes step (4) fusion to obtain merges height field and drives the shallow water equation simulation process based on physics, and the shallow water equation simulated altitude field that simulation generates and water surface fusion height field further merge, and obtain final reconstructed results height field output.
Every group of method for reconstructing that camera adopts voxel segmentation to reject in described step (3), its concrete steps are: water body place Spatial Rules subdivision, be three-dimensional voxel one by one, each voxel center spot projection is organized to all cameras to this, utilize the mapping relations of pixel and water-bed chessboard lattice point to determine the water-bed point corresponding with projected pixel, by camera photocentre, voxel center, water-bed these three points of point and refraction law, tried to achieve the refraction process vector at voxel center point place; For the identical a plurality of voxels of central point horizontal coordinate, only retain the voxel that required normal vector direction is the most consistent, all the other voxels are rejected; And then the voxel staying is proceeded to segmentation as above and reject process, until voxel size is less than given threshold value.
The method of a plurality of water surface elevations field Weighted Fusion on the same group not in described step (4), its objective is and reduce the reconstruction error that different cameral picture noise brings, its concrete steps are: first utilize every group of water surface elevation field, water-bed X-comers re-projection is imaged onto to any one camera in this group, the deviation of calculating the corresponding angle point of re-projection imaging point and original image, is re-projection error; Water surface region is divided into a plurality of subregions, and the water surface in each region merges height field and is merged and formed by the corresponding same area partial weighting in every group of water surface elevation field, and the water surface elevation field weight setting that re-projection error is little is larger.
In described step (5), utilize the water surface to merge the method that height field drives the shallow water equation simulation process based on physics, its objective is toward the water surface and merge the details effect of adding shallow water equation simulated altitude field in height field, obtain True Data and support the abundant reconstructed results height field of details simultaneously, its concrete steps are: the water surface that the described method of step (4) merges the initial time obtaining merges height field as the primary iteration height field of the shallow water equation analog approach based on physics.For each follow-up sampling instant point of camera, the described method of step (4) is merged to the water surface obtaining and merge height field and the further Weighted Fusion in shallow water equation simulated altitude field, obtain the reconstructed results height field output in this moment, reconstructed results height field is simulated the primary iteration height field of next iteration step simultaneously as the shallow water equation based on physics, drive the shallow water equation simulation subsequent process based on physics.
Compared with prior art, the invention has the beneficial effects as follows:
(1) by the process of image reconstruction water surface model from voxel space, counting yield is than the height from image space;
(2) many groups water surface elevation field Weighted Fusion that synchronization camera is rebuild, reduces the reconstruction error that picture noise brings;
(3) result merging between many groups water surface elevation field further merges with shallow water equation simulated altitude field, increases the details effect of the water surface;
(4) can generate existing True Data support and have again the dynamic water surface model that enriches details effect.
Accompanying drawing explanation
Fig. 1 is flow chart of steps of the present invention;
Fig. 2 is dynamic water surface data collector figure of the present invention;
Fig. 3 is that the water surface of the present invention merges the shallow water equation simulation process schematic diagram of height field driving based on physics.
Embodiment
Below in conjunction with drawings and Examples, the invention will be further described.
The present invention proposes a kind of dynamic water surface method for reconstructing based on sampled data, from real world collection, rebuilds and obtains the reusable dynamic water surface model in virtual reality technology field.Specific implementation process as shown in Figure 1, comprise build water surface elevation field in the grouping of data collector and dynamic water surface data acquisition, Image Feature Point Matching, a plurality of cameras and every group reconstruction, organize Weighted Fusion between water surface elevation field more, utilize the water surface to merge height field to drive the steps such as shallow water equation simulation process based on physics.
1, build dynamic water surface data collector, device as shown in Figure 2.With a glass water tank, hold transparent pure water, black and white gridiron pattern plate of water-bed fixed placement, four industrial cameras are fixed on water tank top surrounding, and dried up about 1m left and right, gathers water-bed cross-hatch pattern picture facing to the water surface.Allow the image-region of full each camera of black and white gridiron pattern plate area filling, to increase pixel utilization factor as far as possible.Black and white chessboard ruled paper plates plastic cement diaphragm, then pastes and is fixed on a surface plate.This piece black and white gridiron pattern plate is both for the demarcation of four cameras, also for the collection of dynamic water surface data.In data acquisition, the direct input of black and white gridiron pattern plate is fixed on water tank bottom and is directly contacted with water, avoids light birefringence.Four cameras are controlled PC with the strict synchronous acquisition of mode of parallel port external trigger by one, and view data is stored in PC memory by 1394 lines, has gathered rear unloading hard disk.Four cameras are carried out to inside and outside ginseng to be demarcated.The sampling frame per second of camera is more preferably greater than 30fps, and sampling resolution is as far as possible high, recommends higher than 800*600.Black and white gridiron pattern grid size can not be too large, recommends 10mm * 10mm.The inside and outside ginseng of camera is poured pure water into after having demarcated in glass water tank, and the degree of depth of water is unsuitable excessive, recommends in 10cm.Manufacture the dynamic water surface moving scene that needs collection.Such as dripping directly over glass water tank with dropper, water droplet is fallen the scene of the diffusion of formation rule ripple in water tank, or with hair-dryer, above water tank, manufacture RANDOM WIND to, form the disorderly water surface scene of moving of the water surface.Four cameras start the whole water surface motion process of synchronous acquisition from the static moment of the water surface, and the image sequence collecting is processed for subsequent step.
2, Image Feature Point Matching.Transparent water body does not have obvious unique point, so the angle point of the water-bed cross-hatch pattern picture of reconstruction algorithm utilization is rebuild as feature.The initial time image that the water surface is static detects X-comers by Harris Corner Detection Algorithm, and follow-up time chart image angle point is followed the trail of and determined by Lucas-Kanade optical flow method.The inside and outside ginseng of utilizing camera calibration to obtain, can determine the one-to-one relationship of each image angle point of each camera and water-bed X-comers.And then the mapping relations between image angle point are carried out to bilinear interpolation, obtain the mapping relations by pixel.
3, the reconstruction of the grouping of a plurality of cameras and every group of interior water surface elevation field.Four camera transpostion intervals divide into groups between two, and every group of interior two cameras are rebuild separately.Reconstruction algorithm adopts voxel carving and segments gradually the thinking of search, and concrete steps are: (1) voxel subdivision.Supposing that water-bed gridiron pattern place plane is x-y plane, is equal-sized three-dimensional cube voxel one by one water body place Spatial Rules subdivision, a cubical face and x-y plane parallel.(2) voxel re-projection, computing method vector.For each three-dimensional voxel, suppose that the center is on the water surface.This voxel center spot projection is organized to all cameras to this, utilize the mapping relations of projected pixel and water-bed point to determine water-bed point, by camera photocentre, voxel center, water-bed these three points of point and refraction law, tried to achieve the water surface normal vector of this voxel center.(3) voxel is rejected.For the identical voxel of centre coordinate x-y value, required two the most consistent voxels of normal vector direction after a retaining projection to two camera, all the other voxels are rejected.(4) segmentation search is optimized.To rejecting voxel recycling above-mentioned (1) (2) (3) step staying after step, until voxel size is less than given threshold value, the voxel now staying is the voxel that approaches the water surface most.Connect all voxel center that stay, obtain continuous water surface elevation field.
4, organize the Weighted Fusion between water surface elevation field more, obtain the water surface and merge height field.First utilize every group of water surface elevation field, water-bed X-comers re-projection is imaged onto to any one camera in this group, calculate the deviation of the corresponding angle point of re-projection imaging point and original image, this is re-projection error.Utilize re-projection error to pass judgment on the degree of accuracy of water surface elevation field.If re-projection error is less, corresponding water surface elevation field rebuilds more accurate.Water surface region is divided on x-y coordinate to a plurality of subregions, the water surface of every sub regions merges height field and is merged and formed by the corresponding same area partial weighting in every group of water surface elevation field, and the water surface elevation field weight that re-projection error is little is larger.Specifically, for every sub regions, first calculate the average re-projection error of all re-projection points in this region.If M
1(x, y) and M
2(x, y) represents respectively first group and second group of water surface elevation value corresponding to point that water surface elevation field is (x, y) in horizontal coordinate.Weighted Fusion process is carried out according to the following equation:
M (x, y)=α M
1(x, y)+(1-α) M
2(x, y) (formula 1)
Wherein α (0< α <1) merges weight, and M (x, y) is fusion results.Every sub regions of constantly dividing for each, if the re-projection error of first group of camera is greater than the re-projection error of second group of camera, 0< α <0.5; If equate, α=0.5; Otherwise 0.5< α <1.For all angular coordinate values (x, y) in this subregion, according to above-mentioned formula 1, merge.All subregions all carry out above-mentioned fusion process, finally obtain each water surface constantly and merge height field.
5, utilize the water surface to merge height field and drive the shallow water equation simulation process based on physics.Above-mentioned reconstruction is merged the water surface fusion height field obtaining and is further merged with the shallow water equation simulated altitude field based on physics, to increase details effect.Fusion process as shown in Figure 3.
Shallow water equation (Shallow Water Equation, SWE) is a physical equation describing motion of shallow:
Z
t=-(z
xu+z
yv+z (u
x+ v
y)) (formula 2)
In formula, t is the time, and x, y are two horizontal axis, and z is water surface elevation, and u, v are the component of speed in x, two coordinate axis of y, and subscript represents partial derivative.Formula (2) shows, the variation of water surface elevation is subject to two drivings: one is the convection current-(z of horizontal velocity
xu+z
yv), another is two-dimentional Divergence Field-z (u
x+ v
y).
Shallow water equation analogy method based on physics is utilized computer numerical solution formula 2, obtains each position of water surface elevation field constantly.Above-mentioned the 4th step initial time camera reconstruction is merged to the water surface obtaining and merge height field as the primary iteration height field of shallow water equation simulation, for the iteration of follow-up simulation steps.For each follow-up sampling instant point of camera, camera reconstruction is merged to the water surface fusion height field and the shallow water equation simulated altitude field that obtain and be weighted fusion, fusion process is carried out according to following formula:
M (x, y)=α M
c(x, y)+(1-α) M
s(x, y) (formula 3)
M wherein
c(x, y) and M
s(x, y) represents that respectively the water surface merges height field and shallow water equation simulated altitude field is water surface elevation corresponding to (x, y) some place in horizontal coordinate, and α is for merging weight.Now the bad contrast water surface merges the error of height field and shallow water equation simulated altitude field, and therefore weight factor can be set is α=0.5, is the equal of the mean value of getting two height fields.To all sampled points in whole water surface region, utilize formula 3 to be weighted summation, obtain the reconstructed results height field in this moment.The primary iteration height field of while using this reconstructed results height field constantly as the next iteration step of the shallow water equation based on physics, for driving the shallow water equation simulation subsequent process based on physics.
Conventionally the iteration time step-length of the simulation of the shallow water equation based on physics is shorter than the time interval of camera sampling; therefore the iterative step in the middle of two camera sampling instants for the shallow water equation simulation process based on physics; can, by as shown in Figure 3, camera be rebuild to two waters surface that merge and merge height field M
1and M
2interpolation, the mid-module that interpolation obtains and shallow water equation simulated altitude field are according to formula 3 weighted sums, as shallow water equation, simulate the primary iteration height field of next iteration step, be equivalent to like this drive with True Data each iterative step that has retrained shallow water equation, make analog result more reliable.
For the image of each sampling instant of camera, all according to above-mentioned steps 2,3,4,5, rebuild, rebuild continuous a plurality of reconstructed results height fields in the time series obtaining and form final dynamic water surface model.
The part that the present invention does not elaborate belongs to those skilled in the art's known technology.
The above is only the preferred embodiment of the present invention; it should be pointed out that for those skilled in the art, under the premise without departing from the principles of the invention; can also make some improvements and modifications, these improvements and modifications also should be considered as protection scope of the present invention.
Claims (4)
1. the dynamic water surface method for reconstructing based on sampled data, use a plurality of collected by camera dynamic water surface data, camera divides into groups between two, every group of camera rebuild water surface elevation field by the method that voxel segmentation is rejected, between a plurality of water surface elevations field obtaining, be weighted fusion, obtain the water surface and merge height field, the water surface merges height field and further drives the shallow water equation simulation process based on physics, and generating existing True Data support has again the reconstructed results height field that enriches details effect; It is characterized in that comprising the steps:
(1) build the dynamic water surface data collector of polyphaser, the black and white gridiron pattern plate of known dimensions is close to the bottom, a plurality of cameras are placed in water surface top different visual angles around, fixed camera position guarantees that each camera can photograph complete water-bed cross-hatch pattern picture, and between a plurality of cameras, guaranteeing can synchronous acquisition image; A plurality of cameras are carried out to inside and outside ginseng to be demarcated; Manufacture the water surface moving scene that needs collection, polyphaser high-speed synchronous gathers water-bed tessellated image under water surface motion conditions;
(2) initial time image detects X-comers by Harris Corner Detection Algorithm, and follow-up time chart image angle point is followed the trail of and determined by Lucas-Kanade optical flow method; Utilize the inside and outside ginseng of camera, determine the mapping relations of each camera image angle point and water-bed X-comers; And to the mapping relations interpolation between image angle point, obtain the mapping relations by pixel;
(3) camera is divided into a plurality of groups between two, the image pixel of trying to achieve based on step (2) and the mapping relations of water-bed X-comers, the method that each group utilizes voxel segmentation to reject is rebuild and is obtained a plurality of water surface elevations field;
(4) for each sampling instant of step (1) camera, utilize step (3) the water surface elevation field that camera reconstruction does not obtain on the same group to carry out re-projection and calculate re-projection error, to not on the same group a plurality of water surface elevations field be weighted fusion, the water surface elevation field weight that re-projection error is little is larger, obtains the water surface and merges height field;
(5) utilize step (4) reconstruction to merge the water surface obtaining and merge the shallow water equation simulation process of height field driving based on physics, the shallow water equation simulated altitude field that simulation generates and the water surface merge height field and further merge, and obtain final reconstructed results height field output.
2. a kind of dynamic water surface method for reconstructing based on sampled data according to claim 1, it is characterized in that: every group of reconstruction that camera adopts voxel segmentation to reject in described step (3), its concrete steps are: water body place Spatial Rules subdivision, be three-dimensional voxel one by one, each voxel center spot projection is organized to all cameras to this, utilize the mapping relations of pixel and water-bed chessboard lattice point to determine the water-bed point corresponding with projected pixel, by camera photocentre, voxel center, water-bed these three points of point and refraction law, tried to achieve the refraction process vector at voxel center point place; For the identical a plurality of voxels of central point horizontal coordinate, only retain the voxel that required normal vector direction is the most consistent, all the other voxels are rejected; And then the voxel staying is proceeded to segmentation as above and reject process, until voxel size is less than given threshold value.
3. a kind of dynamic water surface method for reconstructing based on sampled data according to claim 1, it is characterized in that: a plurality of water surface elevations field Weighted Fusion on the same group not in described step (4), its objective is and reduce the reconstruction error that different cameral picture noise brings, its concrete steps are: first utilize every group of water surface elevation field, water-bed X-comers re-projection is imaged onto to any one camera in this group, the deviation of calculating the corresponding angle point of re-projection imaging point and original image, is re-projection error; Water surface region is divided into a plurality of subregions, and the water surface in each region merges height field and is merged and formed by the corresponding same area partial weighting in every group of water surface elevation field, and the water surface elevation field weight setting that re-projection error is little is larger.
4. a kind of dynamic water surface method for reconstructing based on sampled data according to claim 1, it is characterized in that: in described step (5), utilize the water surface to merge height field and drive the shallow water equation simulation process based on physics, its objective is toward the water surface and merge the details effect of adding shallow water equation simulated altitude field in height field, obtain True Data and support the abundant reconstructed results height field of details simultaneously, its concrete steps are: described in claim 3, method merges the water surface fusion height field of the initial time obtaining as the primary iteration height field of the shallow water equation analog approach based on physics, for each follow-up sampling instant point of camera, method described in claim 3 is merged to the water surface obtaining and merge height field and the further Weighted Fusion in shallow water equation simulated altitude field, obtain the reconstructed results height field output in this moment, reconstructed results height field is simulated the primary iteration height field of next iteration step simultaneously as the shallow water equation based on physics, drive the shallow water equation simulation subsequent process based on physics.
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Cited By (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN104615664A (en) * | 2015-01-08 | 2015-05-13 | 杭州电子科技大学 | Consistency database system based on water flow states |
CN106934192A (en) * | 2015-12-30 | 2017-07-07 | 北京航空航天大学 | A kind of shallow water equations model water body modeling method of parameter optimization |
CN108510578A (en) * | 2018-03-13 | 2018-09-07 | 北京航空航天大学青岛研究院 | Threedimensional model building method, device and electronic equipment |
CN110335275A (en) * | 2019-05-22 | 2019-10-15 | 北京航空航天大学青岛研究院 | A kind of space-time vectorization method of the flow surface based on ternary biharmonic B-spline |
Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20080088842A1 (en) * | 2006-10-13 | 2008-04-17 | Howard Schultz | System and method for imaging through an irregular water surface |
US20080177519A1 (en) * | 2007-01-23 | 2008-07-24 | Miller Gavin S P | System and Method for Simulating Shallow Water Effects on Arbitrary Surfaces |
CN101930622A (en) * | 2009-09-29 | 2010-12-29 | 北京航空航天大学 | Realistic modeling and drawing of shallow water wave |
US8204725B1 (en) * | 2008-07-25 | 2012-06-19 | Nvidia Corporation | Real-time breaking waves for shallow water simulations |
CN102903101A (en) * | 2012-09-06 | 2013-01-30 | 北京航空航天大学 | Method for carrying out water-surface data acquisition and reconstruction by using multiple cameras |
-
2013
- 2013-11-27 CN CN201310616867.5A patent/CN103700138B/en not_active Expired - Fee Related
Patent Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20080088842A1 (en) * | 2006-10-13 | 2008-04-17 | Howard Schultz | System and method for imaging through an irregular water surface |
US20080177519A1 (en) * | 2007-01-23 | 2008-07-24 | Miller Gavin S P | System and Method for Simulating Shallow Water Effects on Arbitrary Surfaces |
US8204725B1 (en) * | 2008-07-25 | 2012-06-19 | Nvidia Corporation | Real-time breaking waves for shallow water simulations |
CN101930622A (en) * | 2009-09-29 | 2010-12-29 | 北京航空航天大学 | Realistic modeling and drawing of shallow water wave |
CN102903101A (en) * | 2012-09-06 | 2013-01-30 | 北京航空航天大学 | Method for carrying out water-surface data acquisition and reconstruction by using multiple cameras |
Non-Patent Citations (4)
Title |
---|
YUANYUAN DING ET AL: "Dynamic Fluid Surface Acquisition Using a Camera Array", 《IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION,2011》 * |
YUANYUAN DING ET AL: "Recovering specular surfaces using curved line images", 《IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION,2009》 * |
吴献 等: "一种基于邻域传播的水波模拟方法", 《中国科学技术大学学报》 * |
邹玲 等: "液体采集与建模技术综述", 《计算机研究与发展》 * |
Cited By (6)
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---|---|---|---|---|
CN104615664A (en) * | 2015-01-08 | 2015-05-13 | 杭州电子科技大学 | Consistency database system based on water flow states |
CN106934192A (en) * | 2015-12-30 | 2017-07-07 | 北京航空航天大学 | A kind of shallow water equations model water body modeling method of parameter optimization |
CN106934192B (en) * | 2015-12-30 | 2019-11-19 | 北京航空航天大学 | A kind of shallow water equations model water body modeling method of parameter optimization |
CN108510578A (en) * | 2018-03-13 | 2018-09-07 | 北京航空航天大学青岛研究院 | Threedimensional model building method, device and electronic equipment |
CN110335275A (en) * | 2019-05-22 | 2019-10-15 | 北京航空航天大学青岛研究院 | A kind of space-time vectorization method of the flow surface based on ternary biharmonic B-spline |
CN110335275B (en) * | 2019-05-22 | 2023-03-28 | 北京航空航天大学青岛研究院 | Fluid surface space-time vectorization method based on three-variable double harmonic and B spline |
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