Neural Mesh Representation for Real-Time Robotic Navigation
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Solution Overview
Problem
Existing methods for constructing dense representations of three-dimensional environments are computationally intensive, making real-time dense mapping challenging for robotic devices, especially when compared to sparse techniques that require fewer features and are easier to implement in real-time.
Innovation Solution
An image processing system that generates a mesh representation of a scene using a neural network architecture to estimate in-plane positions and vertex depth values, allowing for a compact and efficient representation of the scene's geometry, which includes a neural network for in-plane position estimation and a depth estimation engine to optimize vertex depth values based on image data and in-plane positions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dense mapping techniques are used to generate three-dimensional representations, then the representation accuracy and detail are improved, but the computational requirements and processing time increase significantly
Solution Approach 1:
The patent segments the dense mapping problem into two independent parts: (1) sparse feature extraction and tracking to establish camera poses and key 3D points, and (2) dense point cloud generation from images. This segmentation allows the computationally intensive dense processing to be performed offline using pre-established camera poses, rather than in real-time, thus reducing device complexity while maintaining representation accuracy.
Solution Approach 2:
The patent performs preliminary sparse feature extraction, camera pose estimation, and 3D point establishment before generating the dense representation. By pre-computing camera poses and identifying key features in real-time, the system prepares all necessary information in advance, allowing the dense point cloud generation to be executed efficiently in an offline manner without real-time computational burden.
2Productivity
If sparse techniques are used to generate representations, then the processing speed and real-time performance are improved, but the representation detail and completeness deteriorate
Solution Approach 1:
The patent divides the representation generation into sparse feature extraction (real-time, high speed) and dense point cloud generation (offline, high detail). The sparse technique handles camera pose estimation and key feature tracking at high speed, while the dense technique uses pre-computed poses to generate detailed point clouds offline, thus preserving both processing speed and representation detail in their respective domains.
Solution Approach 2:
The patent applies partial dense mapping by generating dense point clouds only for regions identified by sparse features, rather than processing every pixel in real-time. This partial application of dense techniques achieves high representation detail where needed while maintaining overall processing efficiency through the sparse framework.
Data Source
AI summary
An image processing system configured to obtain a mesh representation of a scene, wherein the mesh representation comprises a plurality of polygons defined by respective vertices associated with an in-plane position, the in-plane position being in a plane comprising a first dimension and a second dimension, and the vertices having an associated vertex depth value in a third dimension different from the first dimension and the second dimension. The image processing system comprises an in-plane position estimation network configured to process image data representative of an image of the scene to estimate the in-plane positions associated with respective vertices of the mesh representation. The image processing system further comprises a depth estimation engine configured to process the in-plane positions and the image data to estimate the associated vertex depth values for the respective vertices of the mesh representation.