CN109410307A - A kind of scene point cloud semantic segmentation method - Google Patents
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Abstract
The invention belongs to technical field of computer vision, provide a kind of scene point cloud semantic segmentation method, design the frame of the extensive intensive scene point cloud semantic segmentation model based on depth learning technology, for the extensive intensive scene point cloud of input, the two-dimensional signal that convolution can be handled directly can be converted by the three-dimensional information of cloud in the case where information is not lost, and complete a task for cloud semantic segmentation in conjunction with the technology that image, semantic is divided.Under this framework, it can effectively solve the semantic segmentation task of extensive intensive scene point cloud.The semantic segmentation result for the scene point cloud that method of the invention obtains can be utilized directly in tasks such as robot navigation, automatic Pilots.And this method effect in the natural scene of unartificial synthesis is especially significant.
Description
Technical field
The invention belongs to technical field of computer vision, more particularly to based on deep learning to extensive intensive point cloud field
The method of scape progress semantic segmentation.
Background technique
The development of modern computer vision is dominate using the method for convolutional neural networks processing two dimensional image.It is successfully
Key factor is convolution being effectively treated on the image.Convolution is defined on regular grid in the picture, the regular grid
Convolution operation is supported extremely efficiently to realize.This characteristic allows to using powerful deep layer architecture come to high-resolution
Large data collection handled.
When analyzing large-scale three-dimensional scenic, the direct extension of the above method is that three are carried out on voxel grid
Tie up convolution.However, this voxel-based method has significant limitation, cube growth and calculating effect including memory consumption
The problems such as rate.For this reason, voxel-based convolutional neural networks are mostly run on the voxel grid of low resolution, this limit
Their precision of prediction is made.Can be by alleviating these problems based on the technology of Octree, the technology is fixed on Octree
Adopted convolution and it is capable of handling slightly high-resolution data.However, these are still not sufficient to ensure that efficiently analysis large-scale three dimensional field
Scape.
The data of the 3D sensor such as RGB-D camera and Li-DAR capture typically represent the surface of object: i.e. one kind is embedded into
Two-dimensional structure in three-dimensional space.The three-dimensional data of this and real voxel form is contrasted, such as medical image.For dividing
Cloud is considered as a kind of potential surface texture of object by the classical feature for analysing such data, and this data is not regarded as body
Element.
The drawbacks of voxel-based three-dimensional data analysis method is obvious.Nearest some researchs are thought, are based on
The three-dimensional data structure of voxel is not the most natural form of Three dimensional convolution, and proposes based on unordered point set, graph structure and ball
The alternative of shape surface texture.Unfortunately, these methods have the defect of its own, such as have limit quick partial structurtes
Perception relies on restrictive topology hypothesis.
(1) three-dimensional point cloud semantic segmentation
The scene understanding of three-dimensional data, including cloud semantic segmentation, have a long history in computer vision.It starts
Property method be based on hand-made feature, it be suitable for aviation Li-DAR data.These methods can also be with advanced frame
Structure combines.The model of graphics, including condition random field is utilized in popular pre- flow gauge.Equally have in recent years
Method for interactive point cloud semantic segmentation is suggested.
(2) development of the deep learning in three-dimensional data
In recent years, the deep learning revolution of computer vision field has had spread over three-dimensional data analysis, some to be used for
The deep learning method of processing three-dimensional data is suggested.
The common expression of three-dimensional data for deep learning is voxel grid.But the time of cube rank and space are multiple
Miscellaneous degree, this run these methods can only with low resolution, and precision is limited.In order to overcome this limitation, people is studied
Member proposes the expression based on layering spatial data structure, and such as Octree and Kd-Tree, they have preferably storage and calculate
Efficiency, therefore can handle the data of higher resolution ratio.
The application of other some deep learning networks is using RGB-D image as input, later with full convolutional neural networks
Or it is handled based on the neural network of figure, but be generally unsuitable for unstructured unknown cloud of sensor visual angle.For
Solution this problem, Boulch et al. using the virtual camera randomly placed from a cloud rendering image, and with these pictures
Training convolutional neural networks.In the more controlled setting with fixed camera visual angle, multiple view method is used successfully to shape
Segmentation, shape recognition and shape synthesis.
Neat et al. to propose a kind of for analyzing the network of unordered cloud, which is independently handled and is made to a progress
With the information of maximum Chi Hualai polymerization context.But it is very weak due to putting communication between, when the network application in
When large scale scene with complex topology, this method can encounter many difficulties.
Summary of the invention
The present invention in order to solve conventional point cloud scene understanding vulnerable to data resolution limitation, the inadequate robust of local feature and
It is difficult to handle the technical problems such as extensive point off density cloud, devises the extensive intensive scene point based on deep learning technology
The frame of cloud semantic segmentation model can incite somebody to action the extensive intensive scene point cloud of input in the case where information is not lost
The three-dimensional information of point cloud is converted into the two-dimensional signal that convolution can be handled directly, and completes in conjunction with the technology that image, semantic is divided
The task of point cloud semantic segmentation.Under this framework, it can effectively solve the semantic segmentation task of extensive intensive scene point cloud.
Technical solution of the present invention:
A kind of scene point cloud semantic segmentation method, steps are as follows:
(1) building of local coordinate system planar convolution: in order to directly construct two-dimensional convolution on cloud, so that model
A local feature for cloud robust can be extracted in the lower situation of computation complexity, a cloud is projected to utilization by the present invention
PCA technology decomposes three coordinate planes generated to cloud, and constructs convolution module respectively in three coordinate planes and come to a cloud
Carry out the extraction of local feature.Local coordinate plane convolution module is described in detail below.
(1.1) local coordinate system plane is estimated:
For point p each in cloud, its local coordinate system plane is estimated by the analysis of covariance of part first；Tool
For body, for meeting | | p-q | | the point set q in a ball domain of < R, the estimation for tangent plane, the side of the tangent plane of point p
To being by covariance matrix ∑qrrTFeature vector determine, r=q-p；It is worth the smallest feature vector and determines tangent plane
Normal vector np, two feature vectors i and j in addition determine the direction of two reference axis of tangent plane；
(1.2) local coordinate system planar convolution:
Local message is extracted in order to carry out convolution operation on cloud, needs three coordinates by each cloud
Plane；The point in ball domain range that the radius from point p is R is indicated with point set q, and q is projected to three coordinates of p respectively
In plane；For each point p, the function that F (p) is point p is defined, for encoded colors, geometrical characteristic or is come in automatic network
The abstract characteristics of interbed；Building for convolution, the tangent plane π of point pp, defining S (u) is the continuous letter in tangent plane on the u of position
Number amount, c (u) is the convolution nuclear parameter on the u of position, wherein u ∈ R2；
Therefore the convolution operation at point p is defined as follows:
(1.3) signal difference:
For tangent plane, signal interpolation target is to estimate to participate in tangent plane with the semaphore F (q) of the neighborhood point set q of p
The semaphore S (u) of each position of convolution algorithm；Q is projected in the tangent plane of p first, generates a projection point set v=
(rTi,rTj)；Definition:
S (v)=F (q) (2)
In this way, point set v is scattered in the plane of delineation；Therefore these semaphores are subjected to interpolation to estimate that S (u) is participating in rolling up
The semaphore of each position of product operation:
∑v(w(u,v)·S(v)) (3)
Here, w (u, v) is the weight of convolution kernel, and meets ∑vW=1；The present invention is inserted using a kind of fairly simple
Value method: arest neighbors (NN) interpolation.In this interpolation strategies,
Finally again to the formula for carrying out tangent plane convolution operation at point p:
Note that the effect of tangent plane herein is more and more implicit: it provides range domain for u, and is convolution kernel w's
Deduction provides the foundation, but does not need clearly to safeguard.This enables the method to support in the point cloud with millions of a points
Upper building depth network.
(1.4) pond layer:
Convolutional network polymerize the signal on larger space region usually using pond layer.The present invention will be by that will put cloud signal
Pond is realized in amount hash to conventional 3D grid.For the point set being scattering into the same grid, it is polymerize by average Chi Hualai
Its semaphore.Consider point set P={ p } and corresponding semaphore { F (p) }.It enables g represent a voxel grid and enables VgIt represents in P
The point set being hashed into g.Assuming that VgNon-empty is then converged to the information of its all the points on one point by average pond:
(2) cloud semantic segmentation module is put:
(2.1) module inputs:
The input of the module is large-scale indoor and outdoor intensive scene point cloud, and putting the quantity of cloud, there is no limit put cloud
Input feature vector includes the information of RGBXYZ, needs to be converted into RGB, D (depth), H (height), N (normal vector) by pretreatment
As input feature vector；
(2.2) module architectures:
Point cloud semantic segmentation module is the convolutional neural networks from coding structure, and effect is realized to input point cloud
The prediction of semantic information, formula are as follows:
Iout=fseg(Iin；θf) (7)
In above formula, IoutIt is prediction of the network to cloud about n classification semantic information, IinIt is input comprising RGBDHN
The scene point cloud of information；fseg() indicates the convolutional neural networks from coding structure, θfIndicate the weight parameter of network model；
It wherein, include 2 pond layers from the encoder of the convolutional neural networks of coding structure, it is therefore an objective to polymerize volume by pond layer
The feature of volume module output and the Spatial Dimension for reducing feature；There can be 3 convolution modules to obtain a little before each pond layer
The local message of cloud；Restore the Spatial Dimension of feature, same packet before each up-sampling layer in decoder by up-sampling layer
Containing 3 convolution modules；Connection is jumped in increase by two between the respective layer of encoder and decoder makes network that mesh be better anticipated
Target details.
In each convolution module, due to using local coordinate system planar convolution that input feature vector can be projected to three planes
It causes the port number of feature to increase by 3 times of redundancies resulted in a feature that, therefore makes first after local coordinate system planar convolution
Feature port number is further expanded 2 times with 1 × 1 convolution, then separates convolution (n single pass volumes using depth
N channel of product core and input feature vector carries out one-to-one convolution operation) decoupling of the realization to redundancy feature, finally use one
1 × 1 convolution kernel comes fusion and compression to feature.
(3) training method
This patent is using the outdoor point cloud contextual data collection of the Semantic3D comprising 8 classifications and comprising 13 classifications
The outdoor scene data set of S3DIS；Model is trained using the method for data-driven, is lacked to solve 3-D data set
Weary problem, scene point cloud in Semantic3D data set and S3DIS data set is rotated horizontally 10 times respectively by this patent will
Sample size increases by 10 times.
Backpropagation and stochastic gradient descent are used from the convolutional neural networks of coding structure in point cloud semantic segmentation module
Method training.The scene point cloud inputted for one uses the cross entropy with class weight as loss function Lseg, benefit
Weight is calculated with formula (8), wherein the weight w of classification iiFor belong in sample classification i point quantity DiWith classes all in sample
The quantity D of other pointkRatio logarithm opposite number, this is prevented to alleviate the class imbalance phenomenon in data set
The training for the point cloud branch distribution network that quantity occupies the majority.
Network overall error is calculated using formula (9), wherein N indicates the number at scene point cloud midpoint, ylIndicate that the output of point l exists
Score corresponding to true classification, wlFor the weight of point l generic.
Obtain training error after, network will be updated the parameter of network along the opposite direction of gradient, iteration until
Convergence.
The present invention is had the significant advantage that compared with the method for same domain for a cloud semantic segmentation task, relatively more intuitive
Way be that entire point cloud scene is subjected to voxelization, then using three-dimensional convolution kernel and combine at full convolution technique
Reason, but since the problems such as dimension explosion and resolution limitations causes the computational efficiency of this method and accuracy to be unable to
To guarantee.Based on the neural network method of multi-layer perception (MLP) when solving the problems, such as some cloud semantic segmentations due to can not effectively mention
Get the local feature of a cloud so as to cause network cannot future position cloud scene well details.
And cloud is regarded a kind of table of object by the semantic segmentation method of extensive intensive scene point cloud proposed by the present invention
Face structure projects to local coordinate system plane by that will put cloud to directly build two-dimensional convolution on cloud, this makes the party
Method can effectively extract the local feature of a cloud under conditions of information lossless, jump over connection by building in a network and make
The textural characteristics and network high-rise semanteme abundant of Network Low-layer can fully be used when being predicted by obtaining model
Feature, so that network be helped preferably to realize to a prediction for cloud scene details.The semanteme for the scene point cloud that this method obtains point
Cutting result can directly utilize in tasks such as robot navigation, automatic Pilots.And this method is in the natural field of unartificial synthesis
Effect is especially significant in scape.
Detailed description of the invention
Fig. 1 (a) is the scene point cloud of a true meeting room, and Fig. 1 (b) is that the semantic segmentation of meeting room scene point cloud is true
Value.
Fig. 2 is a cloud semantic segmentation network structure.Using scene point cloud as input, by convolution, pondization and up-sampling
Deng operation, the semantic segmentation result of scene point cloud is finally entered.
Fig. 3 is the internal structure of each convolution block, is 1. the shape for converting feature vector to local coordinate system planar convolution
2. formula is that three n × 3 × 3 × d tensors are spliced into n × 3 × 3 × 3d tensor, 3. and is 5. 1 × 1 convolution, 4.
It is that depth separates convolution, is compressed to the dimension of tensor.
Specific embodiment
Invention is described in further detail With reference to embodiment, but the invention is not limited to specific implementations
Mode.
A method of semantic segmentation, including network model are carried out to extensive intensive scene point cloud based on deep learning
Training and model operating procedure part.
1. training network model
The semantic segmentation network of the training extensive intensive scene point cloud, it is necessary first to prepare sufficient point cloud data.Often
A scene point cloud sample should include semantic classes information belonging to RGBXYZ and each point.With S3DIS indoor scene data set
For, after data enhance, 2654 scene point cloud samples are shared as training set and 578 samples as verifying collection.
After obtaining enough data sets, it is necessary first to by the preprocessed information for being converted into RGBDHN of the feature of each point
Input as semantic segmentation network.Later by establish Kd-Tree come in Searching point cloud centered on each point, radius R
Ball domain in the information put, solve the local coordinate system of each point using PCA technology, and by the information put in ball domain by projecting
And etc. be converted into the form that can carry out local coordinate system planar convolution.
Training data, is transported to network to be trained by the semantic segmentation network that a cloud is then built according to attached drawing 2 in batches
In, the class weight of each point is calculated according to formula (8) and formula (9) respectively and puts the error of cloud semantic segmentation network, and according to
The iteration that the method for gradient backpropagation carries out parameter updates, and is accelerated using GPU, sets until the error of network is reduced to
Deconditioning within fixed threshold value or when the number of network iteration is met the requirements.
2. cloud semantic segmentation process
The scene point cloud indoor and outdoor for one, a cloud is converted to cloud feeding preprocessing module first can
The form for carrying out local coordinate system planar convolution, is then input to trained point Yun Yuyi for after pretreatment cloud
The semantic information of scene point cloud is obtained in parted pattern.The semantic information of scene point cloud can be then used for automatic Pilot,
In the tasks such as robot navigation.Process is as shown in Fig. 2.
Claims (1)
1. a kind of scene point cloud semantic segmentation method, which is characterized in that steps are as follows:
(1) cloud the building of local coordinate system planar convolution: is projected to three seats for being decomposed and being generated to cloud using PCA technology
Plane is marked, and constructs the extraction that convolution module to carry out a cloud local feature respectively in three coordinate planes；
(1.1) local coordinate system plane is estimated:
For point p each in cloud, its local coordinate system plane is estimated by the analysis of covariance of part first；It is specific next
It says, for meeting | | p-q | | the point set q in a ball domain of < R, the estimation for tangent plane, the direction of the tangent plane of point p is
By covariance matrix ∑qrrTFeature vector determine, r=q-p；It is worth the normal direction that the smallest feature vector determines tangent plane
Measure np, the direction of two reference axis of two feature vectors i and j decision tangent plane in addition；
(1.2) local coordinate system planar convolution:
The point in ball domain range that the radius from point p is R is indicated with point set q, and q is projected to three coordinates of p respectively
In plane；For each point p, the function that F (p) is point p is defined, for encoded colors, geometrical characteristic or is come in automatic network
The abstract characteristics of interbed；Building for convolution, the tangent plane π of point pp, defining S (u) is the continuous letter in tangent plane on the u of position
Number amount, c (u) is the convolution nuclear parameter on the u of position, wherein u ∈ R2；
Therefore the convolution operation at point p is defined as follows:
(1.3) signal difference:
For tangent plane, signal interpolation target is to estimate to participate in convolution in tangent plane with the semaphore F (q) of the neighborhood point set q of p
The semaphore S (u) of each position of operation；Q is projected in the tangent plane of p first, generates a projection point set v=
(rTi,rTj)；Definition:
S (v)=F (q) (2)
In this way, point set v is scattered in the plane of delineation；Therefore these semaphores are subjected to interpolation to estimate that S (u) is participating in convolution fortune
The semaphore for each position calculated:
∑v(w(u,v)·S(v)) (3)
Here, w (u, v) is the weight of convolution kernel, and meets ∑vW=1；With fairly simple interpolation method: arest neighbors (NN)
Interpolation；In this interpolation strategies,
Finally again to the formula for carrying out tangent plane convolution operation at point p:
(1.4) pond layer:
Pond is realized in cloud semaphore hash to conventional 3D grid by that will put；For the point set being scattering into the same grid,
It polymerize its semaphore by average Chi Hualai；Consider point set P={ p } and corresponding semaphore { F (p) }, g is enabled to represent a voxel
Grid simultaneously enables VgRepresent the point set being hashed into g in P；Assuming that VgThe information of its all the points is then passed through average pond Hua Hui by non-empty
Gather on a point:
(2) cloud semantic segmentation module is put:
(2.1) module inputs:
The input of the module is large-scale indoor and outdoor intensive scene point cloud, and putting the quantity of cloud, there is no limit put the input of cloud
Feature includes the information of RGBXYZ, and it is special as input to need to be converted into RGB, depth D, height H, normal vector N by pretreatment
Sign；
(2.2) module architectures:
Point cloud semantic segmentation module is the convolutional neural networks from coding structure, and effect is realized to input point cloud semanteme
The prediction of information, formula are as follows:
Iout=fseg(Iin；θf) (7)
In above formula, IoutIt is prediction of the network to cloud about n classification semantic information, IinIt is input comprising RGBDHN information
Scene point cloud；fseg() indicates the convolutional neural networks from coding structure, θfIndicate the weight parameter of network model；Wherein,
It include 2 pond layers from the encoder of the convolutional neural networks of coding structure, it is therefore an objective to polymerize convolution mould by pond layer
The feature of block output and the Spatial Dimension for reducing feature；There are 3 convolution modules before each pond layer to obtain the office of a cloud
Portion's information；Restore the Spatial Dimension of feature in decoder by up-sampling layer, is equally rolled up comprising 3 before each up-sampling layer
Volume module；Connection is jumped in increase by two between the respective layer of encoder and decoder makes network that the thin of target be better anticipated
Section；
In each convolution module, first using 1 × 1 convolution further by feature after local coordinate system planar convolution
Port number expands 2 times, then decoupling of the convolution realization to redundancy feature is separated using depth, finally using one 1 × 1
Convolution kernel comes fusion and compression to feature；
(3) training method
Using the room of outdoor point the cloud contextual data collection and the S3DIS comprising 13 classifications of the Semantic3D comprising 8 classifications
Outer scene data set；Model is trained using the method for data-driven, in order to solve the problems, such as that 3-D data set lacks,
Scene point cloud in the outdoor scene data set of the outdoor point cloud contextual data collection of Semantic3D and S3DIS is rotated horizontally respectively
10 times by sample size increase by 10 times；
The side of backpropagation and stochastic gradient descent is used in point cloud semantic segmentation module from the convolutional neural networks of coding structure
Method training；The scene point cloud inputted for one uses the cross entropy with class weight as loss function Lseg, utilize formula
(8) weight is calculated, wherein the weight w of classification iiFor belong in sample classification i point quantity DiWith all categories in sample
The quantity D of pointkRatio logarithm opposite number, this is to prevent quantity to alleviate the class imbalance phenomenon in data set
The training of the point cloud branch distribution network to occupy the majority:
Network overall error is calculated using formula (9), wherein N indicates the number at scene point cloud midpoint, ylIndicate the output of point l true
Score corresponding to classification, wlFor the weight of point l generic；
After obtaining training error, network will be updated the parameter of network along the opposite direction of gradient, and iteration is until convergence.
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