Point Cloud Recovery Model Using 2D Image Intermediary
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Solution Overview
Problem
Point cloud data recovery is hindered by occlusion and sparsity due to long distances or data acquisition issues, leading to hollows or sparse data that are not conducive to extracting and partitioning target objects effectively in 3D reconstruction applications.
Innovation Solution
A method and apparatus utilizing deep learning networks to generate a point cloud data recovery model by training with massive image and point cloud data from the same scene, partitioning data based on object attributes, and determining 3D position data from matching images to improve accuracy and universality of point cloud data recovery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If point cloud data is acquired from long distance or with occlusion, then the coverage area increases, but the data becomes sparse and contains hollows
Solution Approach 1:
The patent uses 2D images as an intermediary to guide the recovery of 3D point cloud data. The 2D images provide visual information about occluded regions and object boundaries, which serves as a mediator to infer and recover missing point cloud data in hollow and sparse areas, thereby improving point cloud data quality while maintaining large coverage area
Solution Approach 2:
The patent recovers missing point cloud data by copying and extrapolating from existing point cloud data and corresponding 2D image information. The recovery model learns to generate missing 3D points by referencing the 2D image projections and available point cloud data, effectively copying the structural information to fill hollow regions
2Device complexity
If a single recovery model is used for all point cloud data, then the system complexity is reduced, but the recovery accuracy for different objects decreases
Solution Approach 1:
The patent segments the point cloud data into multiple categories based on object attributes (e.g., ground, building, vegetation, water). Each category has its own dedicated recovery model trained on specific characteristics of that object type. This segmentation allows each model to specialize in recovering specific object types, significantly improving recovery accuracy while maintaining manageable system complexity through modular architecture
Solution Approach 2:
The patent applies different recovery strategies and models to different regions of the point cloud data based on local object characteristics. Each object type (ground, building, vegetation, water) receives customized recovery treatment through its dedicated model, ensuring that the recovery process adapts to the specific properties of each local region rather than applying a uniform approach
3Ease of manufacture
If point cloud data is sparse due to occlusion, then the data acquisition process is simpler, but the effectiveness of target object extraction and partitioning deteriorates
Solution Approach 1:
The patent employs 2D images as an intermediary to bridge the gap between sparse point cloud data and complete object information. The 2D images provide additional visual context about occluded regions, enabling the recovery model to infer missing 3D structures and improve target object extraction efficiency without complicating the data acquisition process
Data Source
AI summary
A method for generating a point cloud data recovery model includes: acquiring at least one 2D image associated with a first point cloud data frame; partitioning the first point cloud data frame into at least one point cloud data set based on attributes of objects in the 2D image; and for each point cloud data set: determining a matching image of the first point cloud data frame from the at least one 2D image; determining 3D position data of a pixel point in the matching image based on the first point cloud data frame and at least one second point cloud data frame; and using 2D position data and the 3D position data of corresponding pixel points in the matching image as training input data and output data of a training model to generate a point cloud data recovery model for the object corresponding to the point cloud data set.