3D Point Cloud Backbone Pre-Training With Occupancy Probabilities
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Solution Overview
Problem
Training neural networks for semantic segmentation or object detection using LiDAR point cloud data is time-consuming and costly due to the need for extensive annotated datasets.
Innovation Solution
A method for pre-training a backbone network using self-supervised learning, where occupancy probabilities are assigned to both original and extended data points, generating feature vectors, and updating network parameters based on predicted and predetermined probabilities to reduce the reliance on annotated data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If extensive annotated datasets are used for training neural network models for semantic segmentation or object detection, then training performance is improved, but training time and cost increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-training the backbone network on large-scale unannotated point cloud data before fine-tuning on smaller annotated datasets. This preliminary pre-training phase allows the network to learn general features from abundant unannotated data, reducing the need for extensive annotated data and accelerating subsequent training on annotated datasets.
Solution Approach 2:
The patent implements self-service through self-supervised learning where the network learns from unannotated data by creating pseudo-labels or using reconstruction tasks. The system serves itself by generating training signals from unannotated data without requiring manual annotations, thereby reducing dependency on costly annotated datasets while maintaining training performance.
2Reliability
If extensive annotated datasets are used for training neural network models, then training performance is improved, but annotation cost increases significantly
Solution Approach 1:
The network performs self-service by learning from unannotated data through self-supervised mechanisms, generating its own training signals without human annotation. This eliminates the need for costly manual annotation while still achieving good training performance through pre-training on large volumes of unannotated point cloud data.
Solution Approach 2:
By performing preliminary pre-training on unannotated data, the network acquires general features before being exposed to annotated data. This preliminary action reduces the amount of annotated data needed for achieving comparable performance, thereby reducing annotation costs while maintaining training effectiveness.
3Measurement precision
If large annotated datasets are used for training, then model accuracy is improved, but data processing time increases
Solution Approach 1:
The backbone network performs preliminary learning on unannotated data to acquire general features and representations. This preliminary action enables the network to process annotated data more efficiently during fine-tuning, achieving high accuracy with reduced processing time since the network already understands basic patterns from the pre-training phase.
Solution Approach 2:
The patent extracts and learns general features from unannotated data during pre-training, separating this foundational learning from the task-specific fine-tuning on annotated data. This extraction allows the model to achieve high accuracy while processing annotated data more efficiently, as the heavy lifting of general feature learning has already been completed.
Data Source
AI summary
A method for pre-training a backbone network for encoding three-dimensional point cloud data into feature vectors includes: encoding a first data point set in a three-dimensional point cloud into a first feature vector set by the backbone network; generating a corresponding plurality of extended data points based on each first data point in the first data point set to obtain a second data point set; for each first data point and the corresponding plurality of extended data points in the second data point set: assigning predetermined occupancy probabilities to the first data point and the corresponding plurality of extended data points, respectively, and generating second feature vectors for the first data point and the corresponding plurality of extended data points, respectively; generating a predicted occupancy probability for each data point based on the second feature vector of each data point in the second data point set; and updating learnable parameters of the backbone network based on the predetermined occupancy probability and the predicted occupancy probability of each data point in the second data point set.