Abstract: In various example embodiments, relevant changes between 3D models of a scene are detected and classified by transforming the 3D models into point clouds and applying a deep learning model to the point clouds. The model may employ a Siamese arrangement of sparse lattice networks each including a number of modified BCLs. The sparse lattice networks may each take a point cloud as input and extract features in 3D space to provide a primary output with features in 3D space and an intermediate output with features in lattice space. The intermediate output from both sparse lattice networks may be compared using a lattice convolution layer. The results may be projected into the 3D space of the point clouds using a slice process and concatenated to the primary io outputs of the sparse lattice networks. Each concatenated output may be subject to a convolutional network to detect and classify relevant changes.

Abstract: In an example embodiment, techniques are provided for 3D object detection by detecting objects in 2D (as 2D bounding boxes) in a set of calibrated 2D images of a scene, matching the 2D bounding boxes that correspond to the same object and reconstructing objects in 3D (represented as 3D bounding boxes) from the corresponding, matched 2D bounding boxes. The techniques may leverage the advances in 2D object detection to address the unresolved issue of 3D object detection. If sparse 3D points for the scene are available (e.g., as a byproduct of SfM photogrammetry reconstruction) they may be used to refine the 3D bounding boxes (e.g., to reduce their size).

Abstract: In an example embodiment, techniques are provided for 3D object detection by detecting objects in 2D (as 2D bounding boxes) in a set of calibrated 2D images of a scene, matching the 2D bounding boxes that correspond to the same object and reconstructing objects in 3D (represented as 3D bounding boxes) from the corresponding, matched 2D bounding boxes. The techniques may leverage the advances in 2D object detection to address the unresolved issue of 3D object detection. If sparse 3D points for the scene are available (e.g., as a byproduct of SfM photogrammetry reconstruction) they may be used to refine the 3D bounding boxes (e.g., to reduce their size).

Abstract: A method and system of providing segmentation of volumetric three-dimensional image data set such as MRA images. Initially, a three-dimensional MRA volume is input. An initial surface S is then generated by thresholding the input. A signed distance function v to S is then generated, where v=v(x,t) and S is the zero level set of v(·,0).