Monocular 3D Scene Model Reconstruction via Latent Diffusion
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Solution Overview
Problem
Existing methods for acquiring three-dimensional scene models from single two-dimensional photographs face challenges in accurately inferring depth and occluded information, leading to multiple plausible models and instability in traditional machine learning approaches.
Innovation Solution
The use of denoising diffusion probabilistic models (DDPMs) in conjunction with neural reconstruction networks to generate three-dimensional models from single photographs, where a latent tensor is used to enhance the reconstruction process, and the network is trained on paired datasets of images and latent tensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional machine learning approaches (standard regression models) are used for monocular acquisition, then the training process is simpler, but the models exhibit regression-to-mean effects and predict averaged answers that do not correspond to plausible solutions
Solution Approach 1:
The patent applies dynamics by using a generative adversarial network (GAN) framework where two neural networks (generator and discriminator) compete and adapt to each other during training. This dynamic competition enables the generator to produce more plausible and diverse 3D scene models rather than converging to an averaged solution, directly addressing the regression-to-mean problem of traditional regression models.
2Manufacturing precision
If denoising diffusion probabilistic models are used to improve prediction plausibility, then the quality of three-dimensional models improves, but the inference process becomes slower due to large number of neural network evaluations
Solution Approach 1:
The patent applies preliminary action by performing denoising in the latent space before final model generation. The diffusion process operates on compressed latent representations rather than full-resolution data, and the generator network then expands these latent representations into complete 3D scene models. This preliminary processing in latent space significantly reduces the computational burden and number of network evaluations required during inference.
Solution Approach 2:
The patent applies dimensionality change by transforming the problem from operating in high-dimensional pixel space to operating in lower-dimensional latent space. The encoder network compresses input images into latent representations, and the diffusion process operates in this reduced dimensionality, thereby reducing resource consumption while maintaining prediction quality.
3Ease of operation
If monocular acquisition is performed to reconstruct three-dimensional models from single photographs, then the ease of operation improves, but the measurement precision of depth and occluded information deteriorates
Solution Approach 1:
The patent applies the intermediary principle by introducing an encoder network that compresses input images into latent representations, which then serve as intermediaries for the diffusion process. This intermediary latent space allows the model to capture essential 3D geometric information from 2D images while reducing noise and ambiguities, thereby improving depth inference accuracy despite the limitations of monocular input.
Data Source
AI summary
Systems and methods for creating a three-dimensional digital model of a scene from a two-dimensional input image of the scene are described. One aspect includes receiving the two-dimensional input image of the scene, and predicting a latent tensor from the input image. The latent tensor comprises three-dimensional geometrical information of the received two-dimensional input image and information about one or more surfaces occluded in the received two-dimensional input image. The latent tensor and the received two-dimensional image may be inputs to the reconstruction neural network that predicts a three-dimensional model. A paired dataset of input images and corresponding latent tensors may be obtained by a joint training of the reconstruction neural network and an encoding neural network that outputs latent tensors from multiview inputs. The process that predicts the latent tensor from the single input image may then be trained on the paired dataset.