3D Point Cloud Backbone Pre-Training via Occupancy Prediction

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 manual annotation of three-dimensional point cloud datasets.

Innovation Solution

A method for pre-training a backbone network using self-supervised learning, where extended data points are generated from initial data points with assigned occupancy probabilities, and learnable parameters are updated based on predicted and predetermined probabilities to reduce the reliance on annotated datasets.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If extensive manual annotation of three-dimensional point cloud datasets is used for training, then training performance is improved, but time consumption and cost increase significantly

Engineering Contradiction:

Improvetraining performanceVSAvoidannotation time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs self-supervised learning by automatically generating occupancy probabilities from the point cloud data itself without external annotations. The backbone network learns to predict occupancy states of data points and extended points, using the data's own structural information as the training signal, thereby eliminating the need for manual annotation while maintaining training effectiveness

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The method pre-trains the backbone network on unannotated point cloud data before fine-tuning on annotated data for downstream tasks. This preliminary self-supervised pre-training phase extracts useful features and occupancy patterns from raw data, reducing the amount of annotated data needed for subsequent task-specific training and improving overall training efficiency

Inventive Principle:

Principle #10Preliminary action

2Reliability

If extensive manual annotation of three-dimensional point cloud datasets is used for training, then training performance is improved, but cost increases significantly

Engineering Contradiction:

Improvetraining performanceVSAvoidannotation cost

Core Design Contradiction:

ReliabilityVSQuantity of substance

Solution Approach 1:

The system performs self-supervised learning by automatically generating occupancy probabilities from the point cloud data itself without external annotations. The backbone network learns to predict occupancy states of data points and extended points, using the data's own structural information as the training signal, thereby eliminating the need for manual annotation while maintaining training effectiveness

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The method pre-trains the backbone network on unannotated point cloud data before fine-tuning on annotated data for downstream tasks. This preliminary self-supervised pre-training phase extracts useful features and occupancy patterns from raw data, reducing the amount of annotated data needed for subsequent task-specific training and improving overall training efficiency

Inventive Principle:

Principle #10Preliminary action

3Loss of time

If reduced annotated datasets are used for training, then time consumption and cost decrease, but training performance may deteriorate

Engineering Contradiction:

Improveannotation timeVSAvoidtraining performance

Core Design Contradiction:

Loss of timeVSReliability

Solution Approach 1:

The system performs self-supervised learning by automatically generating occupancy probabilities from the point cloud data itself without external annotations. The backbone network learns to predict occupancy states of data points and extended points, using the data's own structural information as the training signal, thereby eliminating the need for manual annotation while maintaining training effectiveness

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The method pre-trains the backbone network on unannotated point cloud data before fine-tuning on annotated data for downstream tasks. This preliminary self-supervised pre-training phase extracts useful features and occupancy patterns from raw data, reducing the amount of annotated data needed for subsequent task-specific training and improving overall training efficiency

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentEP4675499A1Method and apparatus for training backbone network

Publication Date: 2026.01.07 ROBERT BOSCH GMBH

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AI summary

The present application relates to a method for pre-training a backbone network for encoding three-dimensional point cloud data into feature vectors, comprising: 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.