Composable Object NeRF for Synthetic Data Generation
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Solution Overview
Problem
The sim-to-real gap in computer vision models trained in simulation often results in degraded performance when applied to real-world scenarios, due to shifts in input distribution. Existing methods like domain randomization require diverse and realistic training data, which is difficult to achieve and may necessitate manual asset creation and specialized expertise.
Innovation Solution
The Composable-Object-Volume NeRF (COV-NeRF) method uses a Neural Radiance Field (NeRF) to generate synthetic data by extracting feature volumes from real images and composing objects into new scenes with varying poses, enabling the creation of photorealistic renderings and supervision for perception tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If domain randomization is used to improve robustness for out-of-distribution inputs, then the model can generalize to different parameters of the simulator, but the training distribution must be both diverse enough to enable generalization and realistic enough that the real world is a plausible sample, which is difficult to achieve in practice
Solution Approach 1:
The patent segments the scene into multiple objects with independent NeRF representations, allowing each object to be extracted and reused independently. This segmentation enables diverse scene compositions without requiring manual creation of each scene, as objects can be组合ed in various configurations from the same learned representations.
Solution Approach 2:
The patent creates synthetic scenes by copying and composing extracted object representations from real images. The NeRF model learns from real images and generates synthetic scenes by reusing these learned object representations, effectively copying the essential visual characteristics while creating novel compositions that bridge sim-to-real gap.
2Manufacturing precision
If manual asset creation and scene construction are used to achieve realistic training data, then the training distribution can be realistic, but this requires specialized expertise and increases the complexity of the system
Solution Approach 1:
The system performs self-service by automatically extracting object representations from real images and generating synthetic scenes without requiring manual asset creation. The NeRF model learns object representations directly from input images and uses these to synthesize training data, eliminating the need for specialized expertise in manual scene construction.
Solution Approach 2:
The patent changes parameters by transforming real images into synthetic scenes through the NeRF model. By adjusting parameters such as viewpoint, lighting, and object composition in the synthetic scene generation process, the system achieves realistic training data while avoiding manual creation complexity.
3Reliability
If a large sim-to-real gap is observed, then it indicates a mismatch between training distribution and real-world distribution, but it can be difficult to know exactly how to improve the simulator
Solution Approach 1:
The patent implements feedback by using real images to train the NeRF model and then using the generated synthetic scenes to train perception models. The performance on real-world data provides feedback that indicates whether the synthetic data adequately bridges the sim-to-real gap, enabling iterative improvement without requiring simulator debugging expertise.
Solution Approach 2:
The synthetic scenes generated by the NeRF model serve as an intermediary between real images and perception model training. This intermediary layer bridges the distribution gap between simulation and reality, allowing perception models to learn from synthetic data that resembles real-world conditions without requiring direct simulation of physical environments.
Data Source
AI summary
Systems, methods, and devices are disclosed herein for generating synthetic data for training computer vision models using an object-composable NeRF model that reduces the sim-to-real gap for perception-based tasks. In one example, a method includes generating a synthetic dataset using the NeRF model, wherein dataset includes both photorealistic renderings and multiple types of 2D and 3D supervision, including depth maps, segmentation masks, and meshes. To generate the dataset, the NeRF model receives as input a real image of a real scene having objects and a background, extracts a feature volume for each object, and renders one or more synthetic scenes using the sampled objects. The method further includes training a perception model based at least in part on the synthetic dataset and controlling a robotic system based at least in part on output from the trained perception model.