3D Scene Reconstruction Fine-Tuning With Shared Neural Decoders
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Solution Overview
Problem
Existing scene reconstruction techniques require training a different neural network from scratch for each scene, leading to high processing and memory requirements due to the need for complex neural networks with numerous learnable parameters.
Innovation Solution
A single trained neural network is used to generate 3D representations for multiple scenes by computing a 3D feature grid from RGBD images, mapping it to a representation, and modifying decoders based on reconstruction loss, allowing reuse of learned parameters across scenes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a different neural network is trained from scratch for each scene, then the 3D representation can be generated for each scene, but the processing resources and memory requirements become prohibitive
Solution Approach 1:
The patent applies universality by training a single neural network model that can process multiple different scenes. The model is trained on a diverse dataset of RGBD images and corresponding 3D representations, enabling it to generalize and generate 3D representations for various scenes without requiring separate models for each scene. This significantly reduces the processing resources and memory requirements compared to training a new model for each scene.
Solution Approach 2:
The patent applies preliminary action by pre-training the neural network model on a comprehensive dataset of RGBD images and their corresponding 3D representations before use. This pre-training phase allows the model to learn general features and patterns that can be applied to new scenes. During actual use, the model only requires fine-tuning or inference rather than training from scratch, thereby reducing the computational resources needed for scene-specific processing.
2Manufacturing precision
If a complex neural network with a vast number of learnable parameters is used, then the 3D representation can be generated effectively, but the processing resources and memory requirements increase
Solution Approach 1:
The patent employs a universal neural network architecture that achieves effective 3D representation generation without requiring a separate complex model for each scene. The single model uses a optimized number of learnable parameters that are shared across all scenes, maintaining high reconstruction quality while reducing overall model complexity and parameter count compared to training scene-specific models.
Solution Approach 2:
The patent applies parameter changes by using a fixed neural network architecture with a controlled number of learnable parameters that are optimized through pre-training on diverse data. Rather than increasing the number of parameters to handle each scene individually, the model achieves effective 3D representation by learning generalizable features during pre-training and adapting to new scenes through inference or fine-tuning with the same parameter set.
3Adaptability or versatility
If multiple trained neural networks are generated for multiple scenes, then each scene can be processed, but the memory required to store parameter values becomes prohibitive
Solution Approach 1:
The patent implements a single universal neural network model that can process multiple different scenes, eliminating the need to store and manage multiple separate model parameter sets. The model is trained on a diverse dataset during pre-training and then used for inference on new scenes with the same parameter set, significantly reducing memory requirements compared to storing multiple trained networks for different scenes.
Solution Approach 2:
The patent applies the principle of discarding and recovering by discarding the need to store multiple complete model parameter sets. Instead, the system pre-trains a single model once and then reuses the same learned parameters for multiple scenes. The model 'recovers' or adapts to different scenes through inference processes rather than requiring storage of multiple parameter sets, thereby reducing memory consumption.
Data Source
AI summary
In various embodiments, a scene reconstruction model generates three-dimensional (3D) representations of scenes. The scene reconstruction model computes a first 3D feature grid based on a set of red, blue, green, and depth (RGBD) images associated with a first scene. The scene reconstruction model maps the first 3D feature grid to a first 3D representation of the first scene. The scene reconstruction model computes a first reconstruction loss based on the first 3D representation and the set of RGBD images. The scene reconstruction model modifies at least one of the first 3D feature grid, a first pre-trained geometry decoder, or a first pre-trained texture decoder based on the first reconstruction loss to generate a second 3D representation of the first scene.