Multi-Modal Embeddings for 3D Point Cloud Querying
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Solution Overview
Problem
Existing 3D scanning technologies produce point clouds that are discrete, incomplete, and unordered, making it difficult to efficiently process and query 3D data, especially for tasks like computer-aided design, augmented reality, and navigation.
Innovation Solution
The system incorporates multi-modal embeddings into an indexed point-cloud data structure with 3D spatial information, allowing for querying and retrieval of 3D-point-cloud data using natural language or image references, and enables global and local scene understanding through instance segmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If point clouds are used to represent 3D scanned data, then the data can be easily visualized and manipulated, but the representation becomes discrete and incomplete, leading to loss of small features and details
Solution Approach 1:
The patent segments the 3D point cloud data into multiple levels of detail or resolution tiers. High-resolution segments preserve small features and details, while lower-resolution segments provide overall structure. This segmentation allows the system to maintain complete information while enabling efficient visualization by selectively rendering different segments at different resolutions.
Solution Approach 2:
The patent introduces additional dimensional information beyond standard 3D coordinates by incorporating multi-modal embeddings that add semantic, categorical, and contextual dimensions to the point cloud data. This enrichment transforms the discrete geometric representation into a multi-dimensional structure that preserves both visual fidelity and semantic completeness without increasing geometric complexity.
2Loss of information
If high-resolution 3D point clouds are captured to preserve all details, then completeness improves, but the data size becomes large and difficult to compress and process
Solution Approach 1:
The patent divides the large 3D point cloud dataset into spatial segments or voxels, processing and compressing each segment independently. This segmentation reduces the computational burden by breaking down the large dataset into manageable chunks while preserving local details. The segmented structure also enables parallel processing and selective compression of different regions based on their importance.
Solution Approach 2:
The patent dynamically adjusts compression parameters, resolution levels, and embedding dimensions based on the specific characteristics of different data regions. Critical regions with small features use higher parameter settings to preserve detail, while less critical areas use aggressive compression. This adaptive parameter adjustment maintains completeness where needed while reducing overall data complexity.
3Device complexity
If unordered point clouds are used to represent 3D scenes, then the data structure remains simple, but querying and retrieval efficiency deteriorates
Solution Approach 1:
The patent performs preliminary organization of point cloud data by pre-computing spatial indexes, multi-modal embeddings, and hierarchical structures during the data ingestion phase. This preliminary action creates an ordered framework that enables efficient querying without requiring complex real-time processing. The pre-organized structure includes spatial relationships, semantic annotations, and access pathways that accelerate retrieval operations.
Solution Approach 2:
The patent introduces multi-modal embeddings as an intermediary layer between the raw unordered point cloud data and the query system. These embeddings act as a mediator that translates geometric data into semantic representations, enabling efficient querying through semantic matching rather than brute-force geometric comparison. This intermediary structure maintains simplicity in the core data storage while dramatically improving query efficiency.
4Productivity
If multi-modal embeddings are incorporated into indexed point-cloud data structures, then querying and retrieval efficiency improves, but the device complexity and processing requirements increase
Solution Approach 1:
The patent designs the multi-modal embedding structure to serve multiple functions simultaneously: spatial indexing, semantic classification, query matching, and data compression. This universal structure eliminates the need for separate specialized data structures for each function, reducing overall system complexity despite the enhanced capabilities. The same embedding framework handles diverse query types without requiring additional processing infrastructure.
Data Source
AI summary
A method includes incorporating information from multi-modal embeddings in an indexed point-cloud data structure with three-dimensional (3D) spatial information for a captured scene. The method also includes performing at least one of querying or retrieving 3D-point-cloud data based on a user input including at least one of natural language or image references. The method also includes enabling global scene understanding and local scene understanding using instance segmentation in conjunction with the multi-modal embeddings.