Multi-Modal Embeddings for LiDAR Object Search
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous vehicles face challenges in efficiently searching and extracting relevant training examples from large datasets of LiDAR point cloud data, particularly due to the difficulty in distinguishing between objects with similar point clouds.
Innovation Solution
The use of multi-modal embeddings that represent latent characteristics of objects across different sensor modalities, allowing for the search of point cloud data using text, image, or point cloud inputs by identifying embeddings closest in Euclidean space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional point cloud search methods are used, then the system can process LiDAR data, but it fails to accurately distinguish between objects with similar point clouds
Solution Approach 1:
The patent introduces multi-modal embeddings as an intermediary representation layer between raw point cloud data and object identification. These embeddings transform complex LiDAR point cloud data into a standardized vector space that can be efficiently compared and matched, serving as a mediator that simplifies the distinction between similar objects while maintaining accuracy.
Solution Approach 2:
The system changes the parameter representation of objects by converting physical point cloud coordinates into abstract embedding vectors that capture semantic characteristics. This parameter transformation enables more effective differentiation of similar objects by emphasizing discriminative features rather than raw spatial coordinates.
2Reliability
If multi-modal embeddings are used to improve object detection accuracy, then the system can better distinguish similar objects, but the processing complexity increases
Solution Approach 1:
The system performs preliminary embedding generation and storage for training examples before the actual search operation. By pre-computing and storing multi-modal embeddings of training point clouds in the database, the system avoids repeated complex processing during runtime, thus improving detection reliability while managing processing complexity through advance preparation.
Solution Approach 2:
The patent creates simplified copy representations (embeddings) of complex point cloud data. Instead of directly comparing raw point cloud data which is computationally intensive, the system works with compressed embedding vectors that replicate the essential characteristics needed for accurate object distinction, thereby reducing processing complexity while maintaining reliability.
3Productivity
If the system searches through large LiDAR datasets using traditional methods, then it can process the data, but it takes excessive time to find relevant training examples
Solution Approach 1:
The patent replaces traditional mechanical point cloud comparison methods with a learned embedding-based similarity search system. Instead of computationally intensive direct point cloud matching, the system uses neural network-generated embeddings with efficient vector similarity calculations (e.g., cosine similarity), dramatically improving search efficiency and reducing time loss while processing large LiDAR datasets.
Data Source
AI summary
Aspects of the disclosed technology provide solutions for searching point cloud data, such as Light Detection and Ranging (LiDAR) data and in particular, for using multi-modal embeddings for searching objects within a LiDAR data set. A process of the disclosed technology can include steps for receiving road data, wherein the road data represents a real-world environment encountered by an autonomous vehicle (AV) and wherein the road data comprises point cloud data representing a plurality of objects and generating, for each of the plurality of objects, a corresponding set of first embeddings. The process can further include steps for receiving a text string corresponding to a searched object, generating a second embedding corresponding to the searched object and identifying a matching object among the plurality of objects based on a comparison of the set of first embeddings and the second embedding. System and machine-readable media are also provided.