3D Object Detection With State-Space Learning and Dynamic Queries
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Solution Overview
Problem
Existing 3D object detection methods are computationally expensive and lack effective temporal modeling, particularly in sparse sampling scenarios, which can lead to inefficiencies in applications like autonomous driving.
Innovation Solution
Implement state-space spatiotemporal learning and dynamic queries to efficiently sample and process features for 3D object detection, using a state-space model to generate mixed features and identify bounding boxes and classifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional ML models with many millions of nodes are used for 3D object detection, then detection accuracy can be maintained, but computational cost becomes excessively high
Solution Approach 1:
The patent extracts only the essential features needed for 3D object detection by implementing sparse sampling that selects specific points from input images rather than processing all pixels. This extraction approach maintains detection accuracy while dramatically reducing the computational burden by focusing only on relevant feature points.
Solution Approach 2:
The patent segments the feature processing task by dividing it into distinct components: feature extraction from images, sparse sampling to select key points, state-space modeling for temporal relationships, and detection head for final classification. This segmentation allows each component to be optimized independently, reducing overall computational cost while maintaining accuracy.
2Productivity
If sparse sampling is used to reduce computational cost, then processing efficiency improves, but temporal modeling capability deteriorates
Solution Approach 1:
The patent introduces a state-space model as an intermediary component that bridges sparse spatial sampling and temporal modeling. This state-space model maintains temporal relationships between frames by modeling the dynamic states of objects over time, compensating for the reduced spatial information from sparse sampling and preserving temporal modeling reliability.
Solution Approach 2:
The patent transitions from purely spatial feature processing to incorporating temporal dimension through state-space modeling. By adding the time dimension to the feature representation and using recurrent state-space models, the system recovers temporal modeling capabilities that would otherwise be lost due to sparse spatial sampling, enabling efficient processing without sacrificing temporal understanding.
Data Source
AI summary
Systems and techniques are described herein for adjusting weights of a machine learning (ML) model. For instance, a process can include filtering an obtained set of proposal pillars and set of proposal features associated with the set of proposal pillars to obtain a set of sampling points; sampling features from a set of images based on the set of sampling points; masking random features from the sampled features to generate a masked set of features; generating, using a state space model, a state-space representation of the features based on the masked set of features and a predicted set of features; mixing the state-space representation of the features to generate mixed features; identifying a set of bounding boxes associated with objects in the set of images based on the mixed features for output; and generating classifications for the objects in the set of images based on the mixed features for output.