Pseudo-LiDAR 3D Object Localization from Camera Image Sequences
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Solution Overview
Problem
Autonomous vehicles face challenges in determining the three-dimensional location of objects from camera images when lidar sensors are unavailable or malfunctioning, as camera images lack direct three-dimensional information, leading to inaccurate depth estimates from dense depth maps.
Innovation Solution
A system that combines initial depth estimates with image features to generate pseudo-lidar representations, using neural networks to enhance depth predictions by incorporating both pseudo-lidar and image patch features, particularly from multiple camera images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If camera images are used to determine three-dimensional location of objects, then the system can operate without lidar sensors, but the depth estimates become inaccurate because camera images lack direct three-dimensional information
Solution Approach 1:
The patent introduces pseudo-lidar representations as an intermediary that bridges the gap between 2D camera images and 3D spatial information. These pseudo-lidar features are generated from initial depth estimates and serve as a mediator that enables the neural network to infer accurate three-dimensional locations from two-dimensional image data, effectively resolving the contradiction between sensor availability and measurement precision
Solution Approach 2:
The patent transforms two-dimensional camera image data into three-dimensional location predictions by generating pseudo-lidar representations that add depth information. This dimensionality change is achieved through neural networks that process initial depth estimates and image features to produce accurate 3D object locations, allowing the system to operate without direct 3D sensors
2Loss of information
If dense depth maps are generated from camera images, then three-dimensional information can be obtained, but the depth estimates remain noisy and inaccurate
Solution Approach 1:
The patent creates pseudo-lidar representations that copy and enhance the depth information contained in initial depth estimates. Rather than directly using noisy dense depth maps, the system generates refined pseudo-lidar features through neural networks that replicate accurate three-dimensional structure from two-dimensional image data, thereby obtaining 3D information without the noise of direct depth mapping
Solution Approach 2:
The pseudo-lidar representations serve as an intermediary layer between raw camera images and final depth predictions. This intermediary processing through neural networks filters out noise from dense depth maps while preserving and enhancing genuine three-dimensional information, resolving the contradiction between information availability and accuracy
3Measurement precision
If pseudo-lidar features and image patch features are combined, then prediction accuracy improves, but the computational complexity increases
Solution Approach 1:
The patent merges pseudo-lidar features derived from initial depth estimates with image patch features extracted directly from camera images. This combination of two different feature types provides complementary information that significantly improves location prediction accuracy. The neural network integrates these merged features to produce robust three-dimensional location predictions that leverage both geometric and visual information
Solution Approach 2:
The neural network performs multiple functions simultaneously: it processes initial depth estimates to generate pseudo-lidar features, extracts image patch features from camera images, combines these features, and produces final location predictions. This multi-functionality achieves high prediction accuracy while consolidating complexity into a single integrated system rather than separate processing stages
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting three-dimensional object locations from images. One of the methods includes obtaining a sequence of images that comprises, at each of a plurality of time steps, a respective image that was captured by a camera at the time step; generating, for each image in the sequence, respective pseudo-lidar features of a respective pseudo-lidar representation of a region in the image that has been determined to depict a first object; generating, for a particular image at a particular time step in the sequence, image patch features of the region in the particular image that has been determined to depict the first object; and generating, from the respective pseudo-lidar features and the image patch features, a prediction that characterizes a location of the first object in a three-dimensional coordinate system at the particular time step in the sequence.