Semantic Scene Completion for Sparse 3D Data
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Solution Overview
Problem
Existing semantic scene completion techniques are not suitable for processing sparse 3D data from a vehicular context, as they often require fusion of multiple data modalities and are not efficient in real-time applications, especially in poor environmental conditions.
Innovation Solution
A unified neural network that performs semantic completion and segmentation of sparse 3D data using only a single type of sensor, such as a LIDAR sensor, without requiring fusion with 2D image data, enabling real-time processing and reducing computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing SSC techniques are used to process sparse 3D data, then semantic completion can be achieved, but the system requires fusion of multiple data modalities and multiple sensors, increasing device complexity
Solution Approach 1:
The patent extracts and removes the requirement for multiple sensor modalities, achieving semantic scene completion using only sparse 3D point cloud data from a single LIDAR sensor. This eliminates the complexity of sensor fusion while maintaining completion accuracy through a specialized neural network architecture designed for sparse 3D data.
Solution Approach 2:
The neural network is designed to perform multiple functions (semantic segmentation, scene completion, and occupancy prediction) using a single data modality (sparse 3D point clouds), making the system universally applicable without requiring additional sensors or data fusion infrastructure.
2Measurement precision
If existing SSC techniques are used, then semantic completion can be performed, but processing speed is reduced due to fusion of multiple data modalities
Solution Approach 1:
The patent removes the time-consuming sensor fusion process entirely, processing only sparse 3D point cloud data through a optimized neural network. This extraction of unnecessary processing steps significantly increases real-time processing speed while maintaining semantic completion accuracy.
Solution Approach 2:
The neural network architecture is specifically parameterized and optimized for processing sparse 3D data, with modified convolutional operations and feature extraction mechanisms that operate efficiently on point cloud data alone, achieving both high accuracy and real-time processing speeds.
3Reliability
If multiple data modalities are fused, then occupancy inference can be improved, but computational resources increase
Solution Approach 1:
The patent extracts and eliminates the need for multiple data modalities, achieving reliable occupancy inference using only sparse 3D point cloud data. This removes the computational overhead of processing and fusing multiple sensor types while maintaining inference reliability through specialized neural network operations for sparse 3D data.
4Measurement precision
If sensor fusion is performed to compensate for data sparsity, then completion accuracy can be maintained, but system complexity increases
Solution Approach 1:
The patent removes the entire sensor fusion subsystem, achieving completion accuracy on sparse 3D data alone through a neural network architecture specifically designed to handle sparsity. The network incorporates specialized layers and operations that infer missing information from limited point cloud inputs without requiring additional sensor data.
Data Source
AI summary
Methods and systems for performing semantic scene completion of sparse 3D data are described. A frame of sparse 3D data is preprocessed into a sparse 3D tensor and a sparse 2D tensor. A partially completed 3D tensor is generated from the sparse 3D tensor using a 3D prediction network, and a semantically completed 2D tensor is generated from the sparse 2D tensor using a 2D prediction network. The partially completed 3D tensor is completed to obtain a semantically completed 3D tensor by assigning a given class label, which has been assigned to a given pixel in the semantically completed 2D tensor, to a voxel at a corresponding x-y coordinate in the partially completed 3D tensor.