[go: up one dir, main page]

WO2023231173A1 - Procédé de mise en correspondance stéréo binoculaire, dispositif et support de stockage - Google Patents

Procédé de mise en correspondance stéréo binoculaire, dispositif et support de stockage Download PDF

Info

Publication number
WO2023231173A1
WO2023231173A1 PCT/CN2022/110041 CN2022110041W WO2023231173A1 WO 2023231173 A1 WO2023231173 A1 WO 2023231173A1 CN 2022110041 W CN2022110041 W CN 2022110041W WO 2023231173 A1 WO2023231173 A1 WO 2023231173A1
Authority
WO
WIPO (PCT)
Prior art keywords
scale
feature map
map
feature
small
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/CN2022/110041
Other languages
English (en)
Chinese (zh)
Inventor
崔岩
常青玲
戴成林
徐世廷
李敏华
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
China Germany Zhuhai Artificial Intelligence Research Institute Co Ltd
Guangdong Siweikan Intelligent Equipment Co Ltd
4Dage Co Ltd
Wuyi University Fujian
Original Assignee
China Germany Zhuhai Artificial Intelligence Research Institute Co Ltd
Guangdong Siweikan Intelligent Equipment Co Ltd
4Dage Co Ltd
Wuyi University Fujian
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from CN202210647689.1A external-priority patent/CN115222795A/zh
Application filed by China Germany Zhuhai Artificial Intelligence Research Institute Co Ltd, Guangdong Siweikan Intelligent Equipment Co Ltd, 4Dage Co Ltd, Wuyi University Fujian filed Critical China Germany Zhuhai Artificial Intelligence Research Institute Co Ltd
Publication of WO2023231173A1 publication Critical patent/WO2023231173A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/50—Depth or shape recovery
    • G06T7/55—Depth or shape recovery from multiple images
    • G06T7/593—Depth or shape recovery from multiple images from stereo images
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/80—Analysis of captured images to determine intrinsic or extrinsic camera parameters, i.e. camera calibration
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/25—Determination of region of interest [ROI] or a volume of interest [VOI]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks

Definitions

  • the invention relates to the field of image data processing, and in particular to a binocular stereo matching method, equipment and storage medium.
  • Binocular stereo matching is a computer vision task. Binocular stereo matching is also called binocular disparity estimation or binocular depth estimation.
  • the cost volume constructed from the left and right feature maps is usually used to obtain the disparity map or depth map, and then estimate the disparity and depth. , is widely used in fields such as 3D reconstruction, autonomous driving and robot navigation.
  • binocular stereo matching is performed by extracting corresponding feature maps from the left and right images respectively, and constructing a cost volume based on the left and right feature maps.
  • a problem of mismatch in some areas between the two views. easily affects the accuracy of the constructed cost volume, resulting in insufficient accuracy of the obtained disparity map.
  • the present invention aims to solve at least one of the technical problems existing in the prior art.
  • the present invention provides a binocular stereo matching method, equipment and storage medium, which can improve the accuracy of the obtained cost volume and improve the accuracy of the obtained disparity map.
  • a first embodiment of the present invention provides a binocular stereo matching method, which includes the following steps:
  • the content weight of the second feature map is redistributed according to the first feature map, so that the useful information of the second feature map is emphasized and useless information is suppressed.
  • the second large-scale reorganized map can focus on displaying the image information corresponding to each area of the first feature map, thereby improving the matching between the two.
  • the hybrid cost volume constructed based on the first feature map and the second large-scale reorganization map has high accuracy, which can improve the accuracy of the obtained disparity map.
  • the first view and the second view are respectively input to two feature extraction modules to obtain the first feature map and the second feature map, including:
  • Each first initial feature map is sampled to the same scale and then fused to obtain the first feature map
  • Each second initial feature map is sampled to the same scale and then fused to obtain a second feature map.
  • each first initial feature map is sampled to the same scale and then fused to obtain a first feature map, including:
  • Each first initial feature map is sampled to two different scales respectively to obtain two sets of first sampling map groups of different scales, wherein the scale of each first sampling map in the first sampling map group is the same;
  • the two first sampling map groups are fused respectively to obtain the first large-scale feature map and the first small-scale feature map, in which the first large-scale feature map and the first small-scale feature map are both the first feature map, and the first The scale of the large-scale feature map is larger than the scale of the first small-scale feature map.
  • each second initial feature map is sampled to the same scale and then fused to obtain a second feature map, including:
  • Each second initial feature map is sampled to two different scales respectively to obtain two sets of second sampling map groups of different scales, wherein the scale of each second sampling map in the second sampling map group is the same;
  • the two sets of second sampling map groups are fused respectively to obtain the second large-scale feature map and the second small-scale feature map.
  • the second large-scale feature map and the second small-scale feature map are both second feature maps.
  • the scale of the second large-scale feature map is larger than the scale of the second small-scale feature map.
  • the scale of the first large-scale feature map is the same as the scale of the second large-scale feature map
  • the scale of the first small-scale feature map is the same as the scale of the second small-scale feature map
  • the first small-scale feature map and the second small-scale reorganization map are fused and input into the refinement module for feature refinement to obtain a feature refinement map;
  • the first large-scale feature map and distorted feature map are input to the second effective attention module to obtain the second large-scale reorganized map.
  • inputting the first small-scale feature map and the second small-scale feature map into the first effective attention module to obtain the second small-scale reorganized map includes: converting the first small-scale feature map into The feature map and the second small-scale feature map are input to the first effective attention module.
  • the first small-scale feature map is sequentially subjected to maximum pooling, two-dimensional convolution and activation, and then combined with the second small-scale feature map. Multiply the feature maps to obtain the second small-scale reorganization map;
  • Input the first large-scale feature map and distorted feature map into the second effective attention module to obtain the second large-scale reorganized map including: inputting the first large-scale feature map and distorted feature map into the second effective attention module , through the second effective attention module, the first large-scale feature map is sequentially subjected to maximum pooling, two-dimensional convolution and activation, and then multiplied with the second small-scale feature map to obtain the second large-scale reorganized map.
  • a hybrid cost volume is constructed based on the first feature map and the second large-scale reorganization map, including:
  • a subtraction cost volume Based on the first feature map and the second large-scale reorganization map, a subtraction cost volume, a group correlation cost volume and a connection merging cost volume are constructed respectively;
  • the subtractive cost volume, group correlation cost volume and connection merging cost volume are fused to obtain a hybrid cost volume.
  • the hybrid cost volume is input into the disparity regression module to obtain a disparity map, including:
  • a second embodiment of the present invention provides an electronic device, including:
  • a memory a processor and a computer program stored in the memory and executable on the processor.
  • the processor executes the computer program, the binocular stereo matching method of any one of the first aspects is implemented.
  • the electronic device of the embodiment of the second aspect applies any one of the binocular stereo matching methods of the first aspect, it has all the beneficial effects of the first aspect of the present invention.
  • computer executable instructions are stored, and the computer executable instructions are used to execute any one of the binocular stereo matching methods of the first aspect.
  • the computer storage medium of the embodiment of the third aspect can perform any one of the binocular stereo matching methods of the first aspect, it has all the beneficial effects of the first aspect of the present invention.
  • Figure 1 is a main step diagram of the binocular stereo matching method according to the embodiment of the present invention.
  • Figure 2 is a schematic diagram of the working principle of the binocular stereo matching method according to the embodiment of the present invention.
  • Figure 3 is a schematic diagram of the working principle of the multi-scale feature extraction and fusion module in Figure 2;
  • Figure 4 is a schematic diagram of the working principle of the effective attention module in Figure 2;
  • Figure 5 is a schematic diagram of the working principle of the construction process of the hybrid cost body in Figure 2.
  • PSMNet is a major breakthrough. It integrates global context information into the construction of the cost body to solve the ill-posed area problem.
  • FADNet is implemented through a correlation layer based on two-dimensional convolution, and To maintain faster computation speed with the help of a multi-scale weight training strategy, StereoNet uses low-resolution cost volumes to speed up running time, while using an upsampling function with edge-sensing capabilities to preserve edge details.
  • Some learning-based vision tasks such as instance segmentation, scene segmentation and image super-resolution, also perform well using attention algorithms commonly used in natural language processing to focus on regions of interest.
  • Binocular stereo matching is no exception. For example, MCANet uses it to refine disparity, and NLCANet uses it to utilize global context information.
  • binocular stereo matching extracts corresponding feature maps from the left and right images respectively, and constructs a cost volume based on the left and right feature maps.
  • the construction process due to the problem of uncomfortable areas between the two views, the impact The accuracy of the resulting cost volume is constructed, resulting in insufficient accuracy of the obtained disparity.
  • a binocular stereo matching method at least includes the following steps:
  • S500 Input the hybrid cost volume into the disparity regression module to obtain a disparity map.
  • the useful information of the second feature map can be emphasized and the useless information in it can be suppressed, which can improve the obtained second largest scale reorganization.
  • the similarity between the image and the first feature map means that the second large-scale reorganized image can focus on displaying the image information corresponding to each area of the first feature map, thereby improving the relationship between the second large-scale reorganized image and the first feature map.
  • Matching performance, the hybrid cost volume constructed based on the first feature map and the second large-scale reorganization map has high accuracy, which can improve the accuracy of the obtained disparity map.
  • step S200 the first view and the second view are respectively input into two feature extraction modules to obtain the first feature map and the second feature map, including:
  • first feature extraction module and the second feature extraction module are both MobileNetV2 feature extraction modules, which have lightweight characteristics.
  • the corresponding fusion module is constructed through upsampling and downsampling of the U-Net network for Implement the fusion operation of step S230 and step S240.
  • step S230 specifically includes: sampling each first initial feature map to the same scale, fusion, and convolution to obtain the first feature map.
  • step S240 specifically includes: sampling each second initial feature map to the same scale, fusion, and convolution to obtain a second feature map. The performance of the obtained first feature map and the second feature map can be effectively improved through the convolution operation, thereby improving the reliability of subsequent steps.
  • each first initial feature map is sampled to the same scale and then fused to obtain the first feature map, including:
  • each second initial feature map is sampled to the same scale and then fused to obtain a second feature map, including:
  • S242. Fuse the two second sampling map groups respectively to obtain the second large-scale feature map and the second small-scale feature map.
  • the second large-scale feature map and the second small-scale feature map are both second feature maps.
  • the scale of the second large-scale feature map is larger than the scale of the second small-scale feature map.
  • all second sampling maps in the same second sampling map group are fused.
  • the scale of the first large-scale feature map is the same as the scale of the second large-scale feature map
  • the scale of the first small-scale feature map is the same as the scale of the second small-scale feature map
  • Step S300 input the first feature map and the second feature map into the effective attention module to obtain the second large-scale reorganization map, including:
  • the first small-scale feature map and the second small-scale reorganization map are fused and then input into the refinement module for feature refinement to obtain a feature refinement map, where feature refinement refers to a convolution refinement operation of features.
  • the scale of the feature refinement map obtained after feature refinement is the same as the scale of the second largest scale feature map;
  • the second small-scale reorganization graph and the second largest-scale reorganization graph satisfy: in, Represents the second smallest scale reorganized image or the second largest scale reorganized image, Represents the first small-scale feature map or the first large-scale feature map, It is the second small-scale reorganization map or the second largest-scale reorganization map, and i represents the i-th scale.
  • step S310 the first small-scale feature map and the second small-scale feature map are input into the first effective attention module to obtain the second small-scale reorganized map, including: combining the first small-scale feature map and The second small-scale feature map is input to the first effective attention module.
  • the first small-scale feature map is sequentially subjected to maximum pooling, two-dimensional convolution and Sigmoid function activation, and then combined with the second small-scale feature The graphs are multiplied to obtain the second small-scale recombined graph;
  • Step S340 Input the first large-scale feature map and the distorted feature map into the second effective attention module to obtain the second large-scale reorganized image, including: inputting the first large-scale feature map and the distorted feature map into the second effective attention module.
  • the attention module uses the second effective attention module to sequentially perform maximum pooling, two-dimensional convolution and Sigmoid function activation on the first large-scale feature map, and then multiplies it with the second small-scale feature map to obtain the second large-scale reorganization. picture.
  • a single cost body cannot provide sufficient feature information for the model.
  • the subtractive cost body can use the useful information difference between input pairs of images to allow the model to obtain results faster;
  • the group correlation cost body can store the average information of the input pair image channels in groups and stabilize the results. Within a certain range; connect the merged cost body to provide more comprehensive image information for the training model.
  • step S400 constructs a hybrid cost volume based on the first feature map and the second large-scale reorganization map, including:
  • Step S410 According to the first feature map and the second large-scale reorganization map, construct a subtraction cost volume, a grouping correlation cost volume and a connection merging cost volume respectively;
  • Step S420 Fusion of the subtractive cost volume, the grouping correlation cost volume and the connection merging cost volume to obtain a hybrid cost volume.
  • C mix C sub
  • C con C sub
  • N c represents the number of channels of the extracted feature map
  • N g represents the number of groups
  • ⁇ *,*> is the inner product operation
  • represents the connection and merging operation.
  • the specific cost volume is an important link.
  • a new aggregation module is set up to play the role of the hybrid cost volume. It can be understood that the hybrid cost volume is input into the disparity regression module to obtain the disparity map. ,include:
  • C mix′ NCAM(C mix )
  • C mix′ represents the cost aggregation result
  • C mix represents the mixed cost body.
  • first view and the second view where the first view and the second view are the left image and the right image respectively.
  • An initial feature map is sampled to two different scales respectively, and two sets of first sampling images of different scales are obtained, in which the scale of each first sampling image in the same first sampling image group is the same; the two sets of first sampling images are The groups are fused separately to obtain the first large-scale feature map and the first small-scale feature map respectively.
  • the first large-scale feature map and the first small-scale feature map are both the first feature map.
  • the first large-scale feature map is The scale is 1/4, and the scale of the first small-scale characteristic degree is 1/8.
  • each scale is 1/2, 1/4, 1/8, 1/16 and 1/32, where the second feature The extraction module shares the weight with the first feature extraction module; each second initial feature map is sampled to two different scales to obtain two sets of second sampling map groups of different scales, where each second sampling map group in the same
  • the scales of the second sampling images are the same; the two groups of second sampling images are fused respectively to obtain the second large-scale feature map and the second small-scale feature map respectively.
  • the second large-scale feature map and the second small-scale feature The pictures are all second feature maps.
  • the scale of the second largest scale feature map is 1/4, and the scale of the second small scale feature map is 1/8.
  • the first small-scale feature map and the second small-scale feature map are input to the first effective attention module, and the first small-scale feature map is sequentially subjected to maximum pooling, two-dimensional convolution and Sigmoid function activation, and then combined with the second small-scale feature map.
  • the feature maps are multiplied to obtain the second small-scale recombinant map.
  • the above execution process is carried out in the effective attention module on the left side of Figure 2.
  • the execution process of the corresponding effective attention module is shown in Figure 4.
  • the left feature map shown in Figure 4 is the first small-scale feature map.
  • the (distorted) right feature map shown in Figure 4 is the second small-scale feature map, and the new (distorted) right feature map is the second small-scale reorganization map.
  • the first small-scale feature map and the second small-scale reorganization map are fused and then input into the refinement module for feature refinement to obtain the feature refinement map.
  • feature refinement refers to the convolution refinement operation of features.
  • the scale of the feature refinement map obtained by thinning is the same as that of the second largest scale feature map, and the scale is 1/4.
  • the above execution process is carried out in the refinement module in Figure 2.
  • the first large-scale feature map and distorted feature map are input to the second effective attention module.
  • the first large-scale feature map is sequentially subjected to maximum pooling, two-dimensional convolution and Sigmoid function activation, and then combined with the second small-scale feature map. Multiply to obtain the second largest scale reorganization map.
  • the above execution process is carried out in the effective attention module on the right side of Figure 2.
  • the execution process of the corresponding effective attention module is shown in Figure 4.
  • the left feature map shown in Figure 4 is the first large-scale feature map.
  • the (distorted) right feature map shown in Figure 4 is a distorted feature map, and the new (distorted) right feature map is the second largest scale reorganization map.
  • the subtractive cost volume, the grouping correlation cost volume and the connection merging cost volume are constructed respectively; the subtraction type cost volume, the grouping correlation type cost volume and the connection type cost volume are The merged cost bodies are fused to obtain a hybrid cost body.
  • the above execution process is performed on the hybrid cost body in Figure 2, where the construction process of the hybrid cost body is shown in Figure 5.
  • Each of the above processing modules can be obtained through neural network training.
  • the second embodiment of the present invention also provides an electronic device.
  • the electronic device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor.
  • the processor and memory may be connected via a bus or other means.
  • memory can be used to store non-transitory software programs and non-transitory computer executable programs.
  • the memory may include high-speed random access memory and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid-state storage device.
  • the memory may optionally include memory located remotely from the processor, and the remote memory may be connected to the processor via a network. Examples of the above-mentioned networks include but are not limited to the Internet, intranets, local area networks, mobile communication networks and combinations thereof.
  • the non-transient software programs and instructions required to implement the binocular stereo matching method of the above-described first embodiment are stored in the memory.
  • the binocular stereo matching method in the above-described embodiment is executed. For example, execute The above-described method steps S100 to S500, method steps S210 to S240, method steps S231 and S232, method steps S241 and S242, method steps S310 to S340, method steps S410 to S420, and method steps S510 to S520.
  • the device embodiments described above are only illustrative, and the units described as separate components may or may not be physically separate, that is, they may be located in one place, or they may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of this embodiment.
  • a third embodiment of the present invention provides a computer-readable storage medium that stores computer-executable instructions, and the computer-executable instructions are executed by a processor or a controller, for example, by the above-mentioned Execution by a processor in the device embodiment can cause the above-mentioned processor to perform the binocular stereo matching method in the above embodiment, for example, perform the above-described method steps S100 to S500, method steps S210 to S240, and method steps S231 and S232. , method steps S241 and S242, method steps S310 to S340, method steps S410 to S420, and method steps S510 to S520.
  • Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disk (DVD) or other optical disk storage, magnetic cassettes, tapes, disk storage or other magnetic storage devices, or may Any other medium used to store the desired information and that can be accessed by a computer.
  • communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media .

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Evolutionary Computation (AREA)
  • General Health & Medical Sciences (AREA)
  • Software Systems (AREA)
  • Artificial Intelligence (AREA)
  • Computing Systems (AREA)
  • Health & Medical Sciences (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Multimedia (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Computational Linguistics (AREA)
  • Data Mining & Analysis (AREA)
  • Molecular Biology (AREA)
  • General Engineering & Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Medical Informatics (AREA)
  • Databases & Information Systems (AREA)
  • Image Processing (AREA)

Abstract

La présente invention concerne un procédé de mise en correspondance stéréo binoculaire, un dispositif et un support de stockage. Le procédé comprend les étapes consistant à : obtenir une première vue et une seconde vue ; entrer respectivement la première vue et la seconde vue dans deux modules d'extraction de caractéristiques pour obtenir une première carte de caractéristiques et une seconde carte de caractéristiques ; entrer la première carte de caractéristiques et la seconde carte de caractéristiques dans un module d'attention efficace pour obtenir une seconde carte de recombinaison à grande échelle, le module d'attention efficace étant utilisé pour redistribuer un poids de contenu de la seconde carte de caractéristiques selon la première carte de caractéristiques ; construire un volume de coût hybride selon la première carte de caractéristiques et la seconde carte de recombinaison à grande échelle ; et entrer le volume de coût hybride dans un module de régression de disparité pour obtenir une carte de disparités. Selon la présente invention, en fournissant le module d'attention efficace, le poids de contenu de la seconde carte de caractéristiques est redistribué selon la première carte de caractéristiques, la précision du volume de coût hybride construit selon la première carte de caractéristiques et la seconde carte de recombinaison à grande échelle est élevée et la précision de la carte de disparités peut être améliorée.
PCT/CN2022/110041 2022-06-01 2022-08-03 Procédé de mise en correspondance stéréo binoculaire, dispositif et support de stockage Ceased WO2023231173A1 (fr)

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
CN202210616415.6 2022-06-01
CN202210616415 2022-06-01
CN202210647689.1A CN115222795A (zh) 2022-06-09 2022-06-09 双目立体匹配方法、设备及存储介质
CN202210647689.1 2022-06-09

Publications (1)

Publication Number Publication Date
WO2023231173A1 true WO2023231173A1 (fr) 2023-12-07

Family

ID=89026776

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2022/110041 Ceased WO2023231173A1 (fr) 2022-06-01 2022-08-03 Procédé de mise en correspondance stéréo binoculaire, dispositif et support de stockage

Country Status (1)

Country Link
WO (1) WO2023231173A1 (fr)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN119068031A (zh) * 2024-11-05 2024-12-03 大连理工大学 一种基于注意力级联代价体神经网络的双目立体匹配方法
CN120259399A (zh) * 2025-05-21 2025-07-04 北京中科慧眼科技有限公司 针对小目标的双目视差图获取方法、装置与系统

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111259945A (zh) * 2020-01-10 2020-06-09 大连理工大学 引入注意力图谱的双目视差估计方法
CN111340077A (zh) * 2020-02-18 2020-06-26 平安科技（深圳）有限公司 基于注意力机制的视差图获取方法和装置
US20200273192A1 (en) * 2019-02-26 2020-08-27 Baidu Usa Llc Systems and methods for depth estimation using convolutional spatial propagation networks
CN112581517A (zh) * 2020-12-16 2021-03-30 电子科技大学中山学院 双目立体匹配装置及方法
CN114387197A (zh) * 2022-01-04 2022-04-22 京东鲲鹏(江苏)科技有限公司 一种双目图像处理方法、装置、设备和存储介质
CN114445480A (zh) * 2022-01-26 2022-05-06 安徽大学 基于Transformer的热红外图像立体匹配方法及装置

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20200273192A1 (en) * 2019-02-26 2020-08-27 Baidu Usa Llc Systems and methods for depth estimation using convolutional spatial propagation networks
CN111259945A (zh) * 2020-01-10 2020-06-09 大连理工大学 引入注意力图谱的双目视差估计方法
CN111340077A (zh) * 2020-02-18 2020-06-26 平安科技（深圳）有限公司 基于注意力机制的视差图获取方法和装置
CN112581517A (zh) * 2020-12-16 2021-03-30 电子科技大学中山学院 双目立体匹配装置及方法
CN114387197A (zh) * 2022-01-04 2022-04-22 京东鲲鹏(江苏)科技有限公司 一种双目图像处理方法、装置、设备和存储介质
CN114445480A (zh) * 2022-01-26 2022-05-06 安徽大学 基于Transformer的热红外图像立体匹配方法及装置

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN119068031A (zh) * 2024-11-05 2024-12-03 大连理工大学 一种基于注意力级联代价体神经网络的双目立体匹配方法
CN120259399A (zh) * 2025-05-21 2025-07-04 北京中科慧眼科技有限公司 针对小目标的双目视差图获取方法、装置与系统

Similar Documents

Publication Publication Date Title
CN108416327B (zh) 一种目标检测方法、装置、计算机设备及可读存储介质
CN111160214B (zh) 一种基于数据融合的3d目标检测方法
CN111696148A (zh) 基于卷积神经网络的端到端立体匹配方法
WO2022100379A1 (fr) Procédé et système d'estimation d'attitude d'objet sur la base d'une image et d'un modèle tridimensionnel, et support
Choudhary et al. Visibility probability structure from sfm datasets and applications
CN106600583B (zh) 基于端到端神经网络的视差图获取方法
WO2022052782A1 (fr) Procédé de traitement d'image et dispositif associé
CN115731542B (zh) 一种多模态弱监督三维目标检测方法、系统及设备
CN118552596B (zh) 一种基于多视图自监督学习的深度估计方法
CN111047630A (zh) 神经网络和基于神经网络的目标检测及深度预测方法
CN115375746B (zh) 基于双重空间池化金字塔的立体匹配方法
CN111582232A (zh) 一种基于像素级语义信息的slam方法
CN114266879A (zh) 三维数据增强、模型训练检测方法、设备及自动驾驶车辆
CN114299405A (zh) 一种无人机图像实时目标检测方法
CN111626927A (zh) 采用视差约束的双目图像超分辨率方法、系统及装置
WO2023231173A1 (fr) Procédé de mise en correspondance stéréo binoculaire, dispositif et support de stockage
Xiao et al. Level-S $^ 2$ fM: Structure From Motion on Neural Level Set of Implicit Surfaces
CN114998630B (zh) 一种从粗到精的地对空图像配准方法
CN114494395A (zh) 基于平面先验的深度图生成方法、装置、设备及存储介质
El Hazzat et al. Fast 3D reconstruction and modeling method based on the good choice of image pairs for modified match propagation
CN115631223A (zh) 基于自适应学习和聚合的多视图立体重建方法
Abdulwahab et al. Monocular depth map estimation based on a multi-scale deep architecture and curvilinear saliency feature boosting
Cao et al. Fast incremental structure from motion based on parallel bundle adjustment
CN113850293A (zh) 基于多源数据和方向先验联合优化的定位方法
CN116778296A (zh) 基于视角选择和多特征融合的光场显著性目标检测方法

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 22944486

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 22944486

Country of ref document: EP

Kind code of ref document: A1