Pointwise And Instance Segmentation For 3D Point Clouds .
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetPointwise and Instance Segmentation for 3D Point CloudsMS Thesis PresentationSanket GujarWorcester Polytechnic InstituteApril 11, 2019Sanket GujarWPIPointwise and Instance Segmentation for 3D Point CloudsApril 11, 20191 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetSchedule1Overview2Background3Problem Statement4Previous ApproachProjection MethodsPointwise methods5Dataset6Pointer7Pointer SemanticArchitectureResults8Pointer InstanceArchitectureResults9Pointer CapsnetArchitectureResultsSanket GujarWPIPointwise and Instance Segmentation for 3D Point CloudsApril 11, 20192 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetOverviewSanket GujarWPIPointwise and Instance Segmentation for 3D Point CloudsApril 11, 20193 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetMotivationUber’s self driving vehicle hit bicyclist, Perception classification history: 1. Unknown, 2. Vehicle, 3.Bicycle (1.3 secs before impact)Sanket GujarWPIPointwise and Instance Segmentation for 3D Point CloudsApril 11, 20194 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetProblem with CameraSome examples where use of camera for self-driving cars can be dangerous.Sanket GujarWPIPointwise and Instance Segmentation for 3D Point CloudsApril 11, 20195 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetProblem with CameraCameras have limited dynamic range, making detection difficult.Image Ref: Aurora’s Approach to DevelopmentSanket GujarWPIPointwise and Instance Segmentation for 3D Point CloudsApril 11, 20196 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetBetter is LiDARInnovusion LiDAR front projection imageRange up to 200m beneficial for high speed highway driving (9 secs at 50miles/hr).Invariant to lighting conditions same performance in day/night.360 field of view crucial for lane changing and monitoring vehicles behind.Image Ref: An Introduction to LIDAR: The Key Self-Driving Car SensorSanket GujarWPIPointwise and Instance Segmentation for 3D Point CloudsApril 11, 20197 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetBackgroundSanket GujarWPIPointwise and Instance Segmentation for 3D Point CloudsApril 11, 20198 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetPoint cloudsPoint cloud of chair, car, table and airplane from ModelNet10 DatasetPoint cloud: a collection of data points defined by a given coordinates system.Generally produced by 3D scanners, which measure a large number of points onthe external surfaces of objects around them.Used to create 3D CAD models for manufactured parts, for qualityinspection,animation, rendering and mass customization applications.Image Ref: An Introduction to LIDAR: The Key Self-Driving Car SensorSanket GujarWPIPointwise and Instance Segmentation for 3D Point CloudsApril 11, 20199 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetSemantic SegmentationSemantic Segmentation exampleSemantic segmentation is the process of assigning a label to every pixel in animage such that pixels with the same label share certain characteristics.Image Ref: A Review on Deep Learning Techniques Applied to Semantic SegmentationSanket GujarWPIPointwise and Instance Segmentation for 3D Point CloudsApril 11, 201910 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetInstance SegmentationInstance Segmentation exampleInstance segmentation is the process of detecting and delineating each distinctobject of interest appearing in an image.Image Ref: A Review on Deep Learning Techniques Applied to Semantic SegmentationSanket GujarWPIPointwise and Instance Segmentation for 3D Point CloudsApril 11, 201911 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetK-d Treek-d tree examplek-d tree (short for k-dimensional tree) is a space-partitioning data structure fororganizing points in a k-dimensional space.k-d trees are a special case of binary space partitioning trees.The complexity varies from log(N) to N depending on the pruning possible.Image Ref:Using KD-Tree For Nearest Neighbor SearchSanket GujarWPIPointwise and Instance Segmentation for 3D Point CloudsApril 11, 201912 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetK-nearest neighborsNearest neighbours for a point for K 6Nearest neighbor operation for a Tensor of size N x fSanket GujarWPIPointwise and Instance Segmentation for 3D Point CloudsApril 11, 201913 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetProjectionsBird’s eye view and front projections of L shapeBird’s eye view is an elevated view from above, with a perspective as though theobserver were a bird.Its a mapping of all point along z-axis on the x-y plane for our experiments.Front projection is mapping of all points along x-axis on y-z plane.Image Ref: first angle - orthographic projectionSanket GujarWPIPointwise and Instance Segmentation for 3D Point CloudsApril 11, 201914 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetProblem StatementSanket GujarWPIPointwise and Instance Segmentation for 3D Point CloudsApril 11, 201915 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetProblem StatementDevelop an architecture to do end-to-end pointwise andinstance segmentation for 3D point clouds which shouldbe able to handle large point clouds for self-driving vehicleperception stackSanket GujarWPIPointwise and Instance Segmentation for 3D Point CloudsApril 11, 201916 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetPrevious ApproachSanket GujarWPIPointwise and Instance Segmentation for 3D Point CloudsApril 11, 201917 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetProjection MethodsProjection MethodsComplex-YOLO [SMAG18] uses bird’s eye view projection for detectionRun detection and localization network on bird’s eye view or front projectionimages from LiDAR.Projection methods are the fastest for detection and tracking for self-driving stack.Sanket GujarWPIPointwise and Instance Segmentation for 3D Point CloudsApril 11, 201918 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetProjection MethodsProjection MethodsLidar is sensitive enough to detect snow, making it more difficult to identify important objects.If rain drops or snow is picked by LiDAR sensor noise distribution in projectedimages resulting in miss-classification.Miss-classification is a common issue when the vehicles are very close to eachother.Image Ref: Aurora’s Approach to DevelopmentSanket GujarWPIPointwise and Instance Segmentation for 3D Point CloudsApril 11, 201919 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetPointwise methodsPointnetPointnet Architecture [QSMG16]Pointnet was the most successful initial approach to apply deep learning to 3Dpoint clouds.The important feature of the architecture to use symmetric function to getinvariance to certain transformation like rotation and translation.The architecture used spatial and feature transformer to align input points andpoint features.Sanket GujarWPIPointwise and Instance Segmentation for 3D Point CloudsApril 11, 201920 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetPointwise methodsPointnet Pointnet Architecture [QYSG17]Pointnet is a hierarchical network that applies Pointnet recursively on a nestedportioning of the input point cloud.The hierarchical structure is composed of a number of set abstraction levels. Theset abstraction layers consist of three layers: Sampling layer, Grouping layer andPointnet layer.Sanket GujarWPIPointwise and Instance Segmentation for 3D Point CloudsApril 11, 201921 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetPointwise methodsEdge ConvDynamic Graph CNN/ Edge Conv Architecture [WSL 18]EdgeConv appealing property is that it incorporates local neighborhoodinformation as it can be stacked or recurrently applied to learn global shapeproperties.Sanket GujarWPIPointwise and Instance Segmentation for 3D Point CloudsApril 11, 201922 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetPointwise methodsEdge Conv ResultsComparisons of model on Modelnet40. [Ours is EdgeConv here]Sanket GujarWPIPointwise and Instance Segmentation for 3D Point CloudsApril 11, 201923 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetDatasetSanket GujarWPIPointwise and Instance Segmentation for 3D Point CloudsApril 11, 201924 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetModelNet40ModelNet40: Princeton 3D CAD model DatasetModelNet40 DatasetSamplesTrainingTesting94k with 40 labels24k with 40 labelsImage Ref:Princeton ModelNet DatasetSanket GujarWPIPointwise and Instance Segmentation for 3D Point CloudsApril 11, 201925 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetKITTI Vision Benchmark Suite3D bounding box annotations in Kitti Dataset [Gei12]Kitti DatasetSamplesTrainingTestingSanket Gujar74817518WPIPointwise and Instance Segmentation for 3D Point CloudsApril 11, 201926 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetKITTI Readera. Camera Image, b. LIDAR front projection on image with labelsThe Kitti Dataset reader can provide dataset batches for training, doestransformation with the caliberation matrix provided, create Birds eye view, provideinstance and point segmentations labelsSanket GujarWPIPointwise and Instance Segmentation for 3D Point CloudsApril 11, 201927 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetKITTI ReaderThe Kitti Dataset reader can produce instance segmentation labelsSanket GujarWPIPointwise and Instance Segmentation for 3D Point CloudsApril 11, 201928 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetPointerSanket GujarWPIPointwise and Instance Segmentation for 3D Point CloudsApril 11, 201929 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetApproach to the problemThe point can be represented by two properties which is its position in the frame(global) and the distribution of its neighboring points (local).Needed to develop an architecture that can embed both local and global featuresof the point cloud.Would learn to weight the importance of local and global featuresWould generate strong high level features that would make learning faster andeasier.Sanket GujarWPIPointwise and Instance Segmentation for 3D Point CloudsApril 11, 201930 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetPoint cloud featuresPoint cloud featuresGlobal FeaturesLocal featuresConsist of real 3D world coordinatesx, y , z and feature provided by thesensor like intensity, phase of wave,rgb value etc.Is a feature of a single point.Sanket GujarWPIConsist of unit vector pointing from itsneighbours to the point. xi xj , wherexj is the neighbors of the point xi .Is a feature of a single pointdepending on its neighbors.Pointwise and Instance Segmentation for 3D Point CloudsApril 11, 201931 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetPointer featuresPointer feature learningxi is a point in the point clouds and xj is the neighboring point in the pointcloud. we can regard xi as the central pixel and xj : (i, j) ε as a patcharound itWe define global features pij with function gθ which is a parametric non-linearfunction parametrized by the set of learnable parameters θpij gθ (xi , xj )gθ : RF RF RFSanket GujarWPI0Pointwise and Instance Segmentation for 3D Point CloudsApril 11, 201932 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetPointer featuresPointer feature learningxi is a point in the point clouds and xj is the neighboring point in the pointcloud. we can regard xi as the central pixel and xj : (i, j) ε as a patcharound itWe define global features pij with function gθ which is a parametric non-linearfunction parametrized by the set of learnable parameters θpij gθ (xi , xj )gθ : RF RF RF0We define local features qij with function hθ which is also a parametricnon-linear function parametrized by the set of learnable parameters θqij hθ (xi , xi xj )qθ : RF RF RFSanket GujarWPI0Pointwise and Instance Segmentation for 3D Point CloudsApril 11, 201933 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetPointer featuresPointer feature learningGlobal Featurepij gθ (xi , xj )Local Featureqij hθ (xi , xi xj )We define the fusion feature Tij of local features qij and global feature pij withfunction MTij M(pij , qij )Here M is a learnable function which can be weighted sum or a convolutionallayer or a concatenation layerSanket GujarWPIPointwise and Instance Segmentation for 3D Point CloudsApril 11, 201934 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetPointer featuresPointer feature learningGlobal Featurepij gθ (xi , xj )Local Featureqij hθ (xi , xi xj )Fusion featureTij M(pij , qij )Finally, we define the PointerP operation by applying a channel-wise symmetricaggregation operation ( or max)xil 1 Sanket GujarWPI Tijlj:(i,j)Pointwise and Instance Segmentation for 3D Point CloudsApril 11, 201935 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetPointer BlockPointer Main BlockSanket GujarWPIPointwise and Instance Segmentation for 3D Point CloudsApril 11, 201936 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetPointer SemanticSanket GujarWPIPointwise and Instance Segmentation for 3D Point CloudsApril 11, 201937 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetArchitecturePointer Semantic SegmentationPointer Semantic SegmentationSanket GujarWPIPointwise and Instance Segmentation for 3D Point CloudsApril 11, 201938 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetArchitecturePointer Semantic SegmentationPointer Semantic SegmentationImplementation DetailsUsed Skip connections to increase the accuracy model size increases.Loss : Weighted cross-entropy give more loss to target class due to classimbalance.Machine : Turing Cluster Nvidia Pascal P100Training times : approx 2 daysSanket GujarWPIPointwise and Instance Segmentation for 3D Point CloudsApril 11, 201939 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetResultsPointer Semantic SegmentationModel Accuracy on Kitti DatasetModelPointnet EdgeConvPointerSanket GujarWPIAccuracyTarget Class Accuracy97.1295.2094.8845.3475.2083.40Pointwise and Instance Segmentation for 3D Point CloudsApril 11, 201940 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetResultsPointer Semantic Segmentation VisualsSanket GujarWPIPointwise and Instance Segmentation for 3D Point CloudsApril 11, 201941 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetResultsPointer Semantic Segmentation Visuals (Pedestrians)Pointer results (Pedestrains)Sanket GujarWPIPointwise and Instance Segmentation for 3D Point CloudsApril 11, 201942 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetPointer InstanceSanket GujarWPIPointwise and Instance Segmentation for 3D Point CloudsApril 11, 201943 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetArchitecturePointer Clustering Instance SegmentationPointer Instance Segmentation ArchitectureB is the number of clusters formed and batch size which was 20 forexperiments.Bayesian Gaussian MixtureSanket GujarWPIPointwise and Instance Segmentation for 3D Point CloudsApril 11, 201944 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetArchitecturePointer Vector Instance SegmentationPointer Instance Segmentation ArchitectureSanket GujarWPIPointwise and Instance Segmentation for 3D Point CloudsApril 11, 201945 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetResultsPointer Clustering Instance Segmentation VisualsSanket GujarWPIPointwise and Instance Segmentation for 3D Point CloudsApril 11, 201946 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetResultsPointer Vector Instance Segmentation VisualsFigure: Pointer Instance Segmentation resultsPointer Instance Segmentation results linkSanket GujarWPIPointwise and Instance Segmentation for 3D Point CloudsApril 11, 201947 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetResultsPointer Instance SegmentationModel Accuracy on Kitti DatasetModelPointer ClusteringPointer VectorSanket GujarWPIAccuracyTarget Class Accuracy91.5993.3840.6282.91Pointwise and Instance Segmentation for 3D Point CloudsApril 11, 201948 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetResultsFuture WorkEfficient sampling method to reduce the number of inputs pointsReducing the size and inference time of the modelEfficient clustering method for Instance SegmentationSanket GujarWPIPointwise and Instance Segmentation for 3D Point CloudsApril 11, 201949 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetResultsConclusionPointer is more robust and camera independent pipeline for segmenting vehiclesand pedestrian for an autonomous vehicle perception stack.Pointer is invariant to lighting conditions.Pointer is one of the initial approach to do instance segmentation using LIDARdata alone.Pointer can contribute to the development of self-driving vehicle perception stackto make roads more safer for pedestrains.Sanket GujarWPIPointwise and Instance Segmentation for 3D Point CloudsApril 11, 201950 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetResultsReferences IAndreas Geiger, Are we ready for autonomous driving? the kitti visionbenchmark suite, Proceedings of the 2012 IEEE Conference on ComputerVision and Pattern Recognition (CVPR) (Washington, DC, USA), CVPR ’12, IEEEComputer Society, 2012, pp. 3354–3361.Charles Ruizhongtai Qi, Hao Su, Kaichun Mo, and Leonidas J. Guibas, Pointnet:Deep learning on point sets for 3d classification and segmentation, CoRRabs/1612.00593 (2016).Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J. Guibas, Pointnet :Deep hierarchical feature learning on point sets in a metric space, CoRRabs/1706.02413 (2017).Sara Sabour, Nicholas Frosst, and Geoffrey E. Hinton, Dynamic routingbetween capsules, CoRR abs/1710.09829 (2017).Martin Simon, Stefan Milz, Karl Amende, and Horst-Michael Gross,Complex-yolo: Real-time 3d object detection on point clouds, CoRRabs/1803.06199 (2018).Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E. Sarma, Michael M. Bronstein, andJustin M. Solomon, Dynamic graph CNN for learning on point clouds, CoRRabs/1801.07829 (2018).Sanket GujarWPIPointwise and Instance Segmentation for 3D Point CloudsApril 11, 201951 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetPointer CapsnetSanket GujarWPIPointwise and Instance Segmentation for 3D Point CloudsApril 11, 201952 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetCapsule networkConvolutional neural network have the same prediction for both of the images.Internal data representation of a convolutional neural network does not take intoaccount important spatial hierarchies between simple and complex objects.Hinton argued that in order to correctly do classification and object recognition, itis important to preserve hierarchical pose relationships between object parts.Sanket GujarWPIPointwise and Instance Segmentation for 3D Point CloudsApril 11, 201953 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetCapsule networkCNN do not have this capability to understand the change in orientationCapsules encode probability of detection of a feature as the length of their outputvector and the state of the detected feature is encoded as the direction in whichthat vector points to.when detected feature moves around the image or its state somehow changes,the probability still stays the same (length of vector does not change), but itsorientation changes.Sanket GujarWPIPointwise and Instance Segmentation for 3D Point CloudsApril 11, 201954 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetCapsule networkCapsNet Architecture [SFH17]ApproachThe original capsule relies on the existence of a spatial relationship betweenelements in the feature mapWhereas such features are lost in point permutation invariant formulation of 3Dpointwise classification methods.We tried to extend capsule network for 3D point clouds by given the capsules thefeatures extracted by pointer network.Sanket GujarWPIPointwise and Instance Segmentation for 3D Point CloudsApril 11, 201955 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetArchitecturePointer Capsnet ArchitecturePointer Capsnet ArchitectureSanket GujarWPIPointwise and Instance Segmentation for 3D Point CloudsApril 11, 201956 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetResultsPointer Capsnet Results on ModelNet40Pointer Capsnet Accuracy and lossSanket GujarWPIPointwise and Instance Segmentation for 3D Point CloudsApril 11, 201957 / 58
OverviewBackgroundProblem StatementPrevious ApproachDatasetPointerPointer SemanticPointer InstancePointer CapsnetResultsPointer Capsnet Results on ModelNet40Model Accuracy on ModelNet40ModelAccuracyPointnetPointnet EdgeConv3D Capsule (with Edgeconv)Pointer CapsnetSanket GujarWPIPointwise and Instance Segmentation for 3D Point Clouds89.290.792.292.771.29April 11, 201958 / 58
Pointwise and Instance Segmentation for 3D Point Clouds MS Thesis Presentation Sanket Gujar Worcester Polytechnic Institute April 11, 2019 . Image Ref: Aurora’s Approach to Development Sanket Gujar WPI Pointwise and Instance Segmenta
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Fig. 1.Overview. First stage: Coarse segmentation with multi-organ segmentation withweighted-FCN, where we obtain the segmentation results and probability map for eachorgan. Second stage: Fine-scaled binary segmentation per organ. The input consists of cropped volume and a probability map from coarse segmentation.
Internal Segmentation Firewall Segmentation is not new, but effective segmentation has not been practical. In the past, performance, price, and effort were all gating factors for implementing a good segmentation strategy. But this has not changed the desire for deeper and more prolific segmentation in the enterprise.
5 I. Academic Writing & Process . 2. 1 Prepare . 2. 1. 1 What is the assignment asking you to do? What kind of assignment is it? (E.g. essay, research report, case study, reflective
