Gated Video Scene Reconstruction for Low-Light 3D Depth Recovery
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Solution Overview
Problem
Existing methods for large-scale outdoor scene reconstruction in autonomous vehicles face challenges in poorly lit or texture-deficient regions, and scanning lidar sensors struggle with low angular sampling rates, leading to difficulties in recovering expansive real-world scenes.
Innovation Solution
A neural network-based scene reconstruction method using active gated video sequences that incorporates time-gated capture and illumination, leveraging intrinsic depth cues to achieve precise and dense geometry reconstruction regardless of ambient illumination or lighting conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If scanning lidar sensors are used for scene reconstruction, then depth information can be obtained, but the low angular sampling rate makes it difficult to recover expansive real-world scenes
Solution Approach 1:
The patent combines multiple sensing modalities (RGB cameras, depth sensors, lidar) into a unified neural field reconstruction system. By merging these different data sources with complementary strengths, the system overcomes the limitations of individual sensors, particularly the low angular sampling rate of scanning lidar, to achieve comprehensive scene reconstruction.
Solution Approach 2:
The patent introduces a neural field as an intermediary representation that mediates between raw sensor inputs and final scene reconstruction. This neural field acts as a continuous spatial representation that can fill in gaps from sparse lidar data by leveraging correlations from RGB images and depth sensors, effectively resolving the low sampling rate issue.
2Loss of information
If RGB captures are used for scene reconstruction, then appearance information can be obtained, but the method fails in poorly lit or texture-deficient regions
Solution Approach 1:
The patent creates a composite sensing system that combines RGB cameras (for appearance), depth sensors (for geometric structure), and lidar (for accurate depth measurement). This composite approach allows the system to maintain reliable reconstruction in poor lighting conditions by relying on the depth and geometric information from the other sensors when RGB information is insufficient.
Solution Approach 2:
The patent transitions from relying solely on 2D RGB image information to incorporating 3D depth information from multiple sources. By adding this third dimension, the system can reconstruct scenes in poorly lit conditions where 2D RGB information fails, using the depth cues and geometric structure from depth sensors and lidar to compensate for insufficient illumination.
3Measurement precision
If multiple sensor types are combined for scene reconstruction, then reconstruction accuracy improves, but system complexity increases
Solution Approach 1:
The patent designs a unified neural field-based reconstruction framework that can process multiple sensor types (RGB, depth, lidar) through a single coherent model. This multi-functional approach allows the system to handle diverse sensor inputs without requiring separate processing pipelines for each sensor type, thereby managing complexity while maintaining high reconstruction accuracy.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The method provides superior depth synthesis and 3D reconstruction compared to RGB+LiDAR methods, reducing mean absolute error by up to 59.8% and improving intersection of union by up to 11%, while performing view synthesis with a peak signal-to-noise ratio of up to 32.28 dB.
Implementation Method 1
initiate emission of a light pulse by the illuminator... after a predetermined delay from emission of the light pulse, initiate capturing of a plurality of pixels in a scene using the plurality of sensors
Implementation Method 2
based on the plurality of captured pixels, for a point in the scene, compute a respective value for volumetric density, normal, reflectance and ambient light using a corresponding neural field
Implementation Method 3
using the computed respective value for volumetric density, normal, reflectance and ambient light and the computed shadow component, construct a gated image through a volume rendering formulation
Data Source
AI summary
The application generally relates to a computing system including at least one processor. The at least one processor is configured to execute instructions stored in at least one memory to: initiate emission of a light pulse by an illuminator, after a predetermined delay from emission of the light pulse, initiate capturing of a plurality of pixels in a scene using a plurality of sensors based on the plurality of captured pixels, for a point in the scene, compute a respective value for volumetric density, normal, reflectance and ambient light using a corresponding neural field. The processor further, based upon the emitted light pulse, computes a shadow component corresponding to an origin of the illuminator and a direction of the emitted light pulse and using the computed respective value for volumetric density, normal, reflectance and ambient light and the computed shadow component, constructs a gated image through a volume rendering formulation.