Monocular Depth Estimation via Packing and Unpacking Blocks
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Solution Overview
Problem
Robotic devices face challenges in determining depth information from monocular images due to limited resolution, image artifacts, and difficulties in training processing routines, leading to reduced situational awareness and navigation difficulties.
Innovation Solution
A depth system utilizing a machine learning model with encoding layers configured with packing blocks and decoding layers with unpacking blocks, which retain and resolve spatial information through skip connections and sub-pixel convolutions to generate high-resolution depth maps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional monocular image processing is used, then the system is cost-effective, but depth estimation accuracy is poor due to limited resolution and image artifacts
Solution Approach 1:
The processing system is segmented into distinct functional modules: encoding layers with packing blocks, decoding layers with unpacking blocks, and skip connections. This segmentation allows each module to specialize in specific tasks (feature extraction, information retention, spatial resolution) while collectively achieving high accuracy without requiring complete system redesign
Solution Approach 2:
The patent introduces additional dimensional transformations through packing blocks that convert spatial dimensions to channel dimensions, and unpacking blocks that reverse this transformation. This dimensional manipulation enables the network to retain sub-pixel information and enhance depth estimation accuracy by processing information in multiple representational spaces simultaneously
2Manufacturing precision
If packing blocks are used to retain sub-pixel information, then depth map resolution is improved, but computational processing time increases
Solution Approach 1:
Skip connections establish continuous information flow between encoding and decoding layers, allowing useful spatial information to be preserved throughout the forward and backward propagation processes. This continuity ensures that high-resolution depth map generation does not require complete reprocessing of all layers, thereby reducing overall computational time while maintaining high resolution
Solution Approach 2:
The system dynamically adjusts processing parameters through the packing and unpacking operations, transforming the representation of spatial information at different stages of processing. By changing the dimensional parameters of feature maps through these blocks, the system optimizes the balance between retention of sub-pixel information and computational efficiency at each layer
3Adaptability or versatility
If monocular cameras are used, then the system cost is reduced, but situational awareness is compromised due to limited field-of-view and resolution
Solution Approach 1:
Packing blocks transform spatial information into additional channel dimensions, effectively increasing the information capacity of the feature maps without adding physical sensors. This dimensional expansion allows the system to compensate for the limited field-of-view and resolution of monocular cameras by representing spatial relationships in multiple dimensional spaces
Solution Approach 2:
The architecture employs nested structures where unpacking blocks in decoding layers process and refine information from packing blocks in encoding layers. This nesting allows the system to progressively recover and enhance spatial information that would otherwise be lost in monocular imaging, building situational awareness through hierarchical processing of nested feature representations
Data Source
AI summary
System, methods, and other embodiments described herein relate to generating depth estimates of an environment depicted in a monocular image. In one embodiment, a method includes, in response to receiving the monocular image, processing the monocular image according to a depth model to generate a depth map. Processing the monocular images includes encoding the monocular image according to encoding layers of the depth model including iteratively encoding features of the monocular image to generate feature maps at successively refined representations using packing blocks within the encoding layers. Processing the monocular image further includes decoding the feature maps according to decoding layers of the depth model including iteratively decoding the features maps associated with separate ones of the packing blocks using unpacking blocks of the decoding layers to generate the depth map. The method includes providing the depth map as the depth estimates of objects represented in the monocular image.