Perspective View Range Image Neural Network Processing
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Solution Overview
Problem
Existing neural network approaches for 3D point cloud understanding often operate in 3D space, which can be inefficient and less accurate compared to leveraging the underlying geometry of 2D perspective view range images generated by sensors like LiDAR or RGBD cameras.
Innovation Solution
Adapting a conventional 2D convolutional network architecture with geometry-dependent kernels that exploit the 3D structure of local neighborhoods in the perspective view range space, allowing for more accurate and efficient generation of point cloud understanding outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing neural network approaches operate in 3D space for point cloud understanding, then they can process raw sensor data directly, but they are inefficient and less accurate
Solution Approach 1:
The patent introduces an intermediary representation called perspective view range images that mediate between raw 3D point cloud data and the neural network processing. This intermediate 2D representation preserves geometric information while enabling more efficient and accurate convolutional processing, resolving the contradiction between accuracy and efficiency.
Solution Approach 2:
The patent transforms the problem from 3D space to 2D space by projecting point cloud data onto perspective view range images. This dimensionality change allows the use of efficient 2D convolutional networks while maintaining geometric relationships through carefully designed kernel functions that incorporate 3D spatial information.
2Productivity
If conventional 2D convolutional networks are used directly on perspective view range images, then computation is faster, but they fail to exploit the 3D structure and geometry information
Solution Approach 1:
The patent applies local quality by designing geometry-dependent kernel functions that adapt to the local geometric structure at each position in the perspective view range image. The kernels incorporate range information and angular relationships specific to each local neighborhood, allowing the network to exploit 3D structure while maintaining 2D processing efficiency.
Solution Approach 2:
The patent changes the parameters of the convolutional kernels to be geometry-dependent rather than fixed. The kernel functions dynamically adjust based on range features and angular relationships, enabling the network to capture 3D geometric structures while operating on 2D images for computational efficiency.
Data Source
AI summary
Methods, computer systems, and apparatus, including computer programs encoded on computer storage media, for processing a perspective view range image generated from sensor measurements of an environment. The perspective view range image includes a plurality of pixels arranged in a two-dimensional grid and including, for each pixel, (i) features of one or more sensor measurements at a location in the environment corresponding to the pixel and (ii) geometry information comprising range features characterizing a range of the location in the environment corresponding to the pixel relative to the one or more sensors. The system processes the perspective view range image using a first neural network to generate an output feature representation. The first neural network comprises a first perspective point-set aggregation layer comprising a geometry-dependent kernel.