A point cloud convolution is defined by pull-back of the Euclidean volumetric convolution via an extension-restriction mechanism. The point cloud convolution is computationally efficient, invariant to the order of points in the point cloud, robust to different samplings and varying densities, and ...
PCNN is a novel framework for applying convolutional neural networks to point clouds. The framework consists of two operators: extension and restriction, mapping point cloud functions to volumetric functions and vise-versa. A point cloud convolution is defined by pull-back of the Euclidean volumetric...
Therein, multi- view methods project point clouds onto images of multi- ple views and process them with 2D Convolution Neural Networks (CNN) [22] pre-trained on ImageNet [28], such as MVCNN [49] and others [14, 15, 21, 26, 62]. Normally, such view-projection methods operate on ...
DCNdenotes replacing 3x3 conv with the 3x3 deformable convolution inc3-c5stages of backbone. nonein theanchorcolumn means 2-dcenter point(x,y) is used to represent the initial object hypothesis.singledenotes one 4-d anchor box (x,y,w,h) with IoU based label assign criterion is adopted. ...
The method includes producing a sparse pseudo-image from a point cloud using a feature encoder, using a 2D convolution backbone to process the pseudo-image into high-level, and using detection heads to regress and detect 3D bounding boxes. This work utilizes a...
A convolution-subtraction scatter correction method for 3D PET The method accounts for the 3D acquisition geometry and nature of scatter by performing the scatter estimation on 2D projections. The assumptions of the ... DL Bailey,SR Meikle - 《Physics in Medicine & Biology》 被引量: 406发表: ...
a100 convolution-type 100卷积类型 [translate] aCircumstantial realism, similarly, is provided by the consistency with which the film describes and relates its locations and the creation of the seamless illusion hinges, at a level more basic than psychological characterization, on the two fundamental ...
Convolutions kernels Most point-based convolution networks borrow the common encoder/decoder idea (or encoder only). An encoder operates on a dense point cloud, which is iteratively decimated after each layer or group of layers as we go deeper. The points themselves support feature vecto...
In this work, we propose a new Geometry-Aware Visual Feature Extractor (GAVE) that employs multi-scale local linear transformation to progressively fuse these two modalities, where the geometric features from the depth data act as the geometry-dependent convolution kernels to transform the visual ...
[SDC] Stacked Dilated Convolution: A Unified Descriptor Network for Dense Matching Tasks, CVPR'2019 [pdf] [GIFT] GIFT: Learning transformation-invariant dense visual descriptors via group cnns, NeurIPS'2019 [code] [DISK] DISK: Learning local features with policy gradient, NeurIPS'2020 [code] [...