Point Cloud Panoptic Segmentation via Range Image Projection
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Solution Overview
Problem
Current methods for panoptic segmentation of point clouds, such as top-down and bottom-up approaches, are computationally inefficient and require significant memory and computing resources, with top-down approaches generating redundant information and bottom-up approaches being time-consuming due to clustering methods.
Innovation Solution
A computer-implemented method and system for proposal-free and cluster-free panoptic segmentation that projects a point cloud into a range image, extracts features, downsamples and scales them using local geometry, and performs semantic and instance segmentation using different weights for feature channels, eliminating the need for clustering and object detectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If top-down approach with object detector is used for panoptic segmentation, then instance-level detection capability is improved, but computational efficiency deteriorates and redundant information is generated
Solution Approach 1:
The patent extracts and removes the object detector component from the panoptic segmentation pipeline, eliminating the computationally expensive proposal generation stage while retaining instance-level segmentation capability through direct clustering of semantic segmentation results
Solution Approach 2:
The patent merges the semantic segmentation and instance segmentation processes into a unified pipeline where instance segmentation is derived directly from semantic segmentation results through clustering, eliminating redundant processing stages
2Productivity
If bottom-up approach with clustering method is used for panoptic segmentation, then computational resources are reduced, but processing time increases significantly
Solution Approach 1:
The patent performs preliminary semantic segmentation to generate semantic labels for all points before clustering, which provides a structured foundation that accelerates the subsequent instance segmentation clustering process
Solution Approach 2:
The patent applies different processing strategies to different regions of the point cloud, using geometric-aware downsampling and local feature extraction to optimize clustering performance in different spatial contexts
3Measurement precision
If geometric-aware downsampling with local geometry scaling is applied, then feature representation quality is improved, but processing complexity increases
Solution Approach 1:
The patent segments the point cloud processing into multiple resolution levels through geometric-aware downsampling, where features are extracted at different scales and then fused, improving representation quality while managing complexity through hierarchical processing
Data Source
AI summary
Systems and methods for panoptic segmentation of a point cloud are provided. A point cloud is projected into a range image. Features are extracted from the range image and generating a feature map from the extracted features. The feature map is downsampled and the features are scaled during downsampling using local geometry. Features are extracted from the downsampled feature map. The point cloud is semantically segmented at least partially based on the features extracted. Instances in the point cloud are segmented at least partially based on the features extracted.