Abstract: An intelligent understanding apparatus for real-time reconstruction of a large-scale scene light field includes the following. A data obtaining module obtains a 3D instance depth map, and obtain 3D voxels and voxel color information through simultaneous positioning and map generation. The model constructing module constructs and trains a real-time light field reconstruction network model using a ScanNet dataset. The real-time light field reconstruction network model extracts features of the 3D voxels and voxel color information, and obtain a semantic segmentation result and an instance segmentation result. The semantic segmentation module inputs the 3D voxel and voxel color information corresponding to the 3D instance depth map into the trained real-time light field reconstruction network model, and determine an output as a semantic segmentation result and an instance segmentation result corresponding to the 3D instance depth map.

Abstract: A method for processing three-dimensional (3D) point cloud data based on incremental sparse 3D convolution is provided. A computer device obtains 3D point cloud data and forms a training set by processing the 3D point cloud data. The computer device constructs and trains a sparse 3D convolutional network model by inputting the training set. The computer device constructs an incremental sparse 3D convolutional network model by performing incremental replacement of sparse convolutional layers of the trained sparse 3D convolutional network model. The computer device inputs real-time 3D point cloud data into the incremental sparse 3D convolutional network model, and determines an output result as a result of processing the real-time 3D point cloud data. Processing of the 3D point cloud data at least includes 3D semantic segmentation, target detection, 3D classification and video processing.