3D Dynamic Object Removal With Coarse-to-Fine Scene Reconstruction
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Solution Overview
Problem
Existing machine-learned models for robotic platforms, such as autonomous vehicles, struggle to accurately detect and remove dynamic objects from three-dimensional environments, leading to incomplete scene representations that hinder effective perception and simulation.
Innovation Solution
A machine-learned dynamic object removal model processes multi-modal sensor data from different types of sensors to generate scene representations by removing dynamic objects, utilizing a coarse-to-fine framework that includes geometric, temporal, and intermediate representations to reconstruct occluded static features with fine-grained details.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dynamic objects are removed from three-dimensional sensor data to improve scene representation accuracy, then simulation accuracy and perception quality improve, but computational complexity and processing time increase
Solution Approach 1:
The system segments the complex object removal task into multiple processing stages: initial object detection, candidate region identification, fine-grained reconstruction, and scene synthesis. This multi-stage segmentation allows the system to handle computational complexity in manageable steps while maintaining high scene representation accuracy through progressive refinement at each stage.
Solution Approach 2:
The system performs preliminary actions by pre-processing sensor data to identify and mask dynamic objects before main scene reconstruction. By detecting and removing dynamic objects in advance, the system simplifies subsequent processing steps and reduces the computational burden on the main reconstruction algorithm, thereby balancing accuracy requirements with computational constraints.
2Measurement precision
If multi-modal sensor data from multiple sensors is processed to improve dynamic object detection accuracy, then detection precision improves, but memory usage and processing overhead increase
Solution Approach 1:
The system extracts and processes only the essential features from multi-modal sensor data rather than handling complete raw datasets. By extracting key dynamic object characteristics from camera, LIDAR, and radar sensors separately and then fusing only the necessary feature representations, the system achieves high detection accuracy while significantly reducing memory consumption compared to processing full multi-modal datasets.
Solution Approach 2:
The system employs a unified detection framework that handles multiple sensor types (camera, LIDAR, radar) through a common processing architecture. This multi-functional approach allows the same computational resources to process different sensor modalities, improving resource utilization efficiency and reducing overall memory requirements compared to separate dedicated processing pipelines for each sensor type.
3Manufacturing precision
If fine-grained reconstruction of occluded regions is performed to improve scene realism, then simulation realism improves, but processing time and computational resources increase
Solution Approach 1:
The system dynamically adjusts the level of reconstruction detail based on scene importance and computational constraints. For critical regions requiring high realism, fine-grained reconstruction is applied with detailed geometric and textural restoration. For less critical areas, coarser reconstruction methods are used, thereby optimizing the balance between simulation realism and processing time through adaptive, dynamic resource allocation.
Solution Approach 2:
The system applies fine-grained reconstruction selectively to only those occluded regions that are most important for scene understanding and simulation realism, rather than uniformly processing all occluded areas. This partial action approach focuses computational resources on critical reconstruction tasks, achieving high realism where needed while minimizing overall processing time by skipping or simplifying less critical regions.
Data Source
AI summary
Systems and methods for generating simulation data based on real-world environments are provided. A method includes obtaining multi-modal sensor data indicative of a dynamic object within an environment of a robotic platform. The multi-modal sensor data is associated with a plurality of timesteps including a first timestep and a second timestep. The method includes providing the multi-modal sensor data indicative of the dynamic object within the environment as an input to a machine-learned dynamic object removal model. And, the method includes receiving as an output of the machine-learned dynamic object removal model, in response to receipt of the multi-modal sensor data, a scene representation indicative of at least a portion of the environment including a reconstructed region based at least in part on removal of the dynamic object and multiple levels of granularity. The scene representation is used as a template for generating different simulations within the depicted environment.