Planar Mesh Reconstruction Without Ray Marching
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Solution Overview
Problem
Existing 3D planar reconstruction methods are computationally complex, require ray marching techniques, and rely on costly 2D or 3D plane annotations, leading to inefficiencies and performance degradation with distribution shifts.
Innovation Solution
Perform unsupervised 3D planar reconstruction using neural mesh fusion (NMF) that directly deforms surface meshes through gradient-based optimization and explicit neural rendering, without relying on implicit representations or ray marching, utilizing multi-view geometry constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ray marching techniques are used for 3D planar reconstruction, then reconstruction capability is achieved, but computational complexity increases
Solution Approach 1:
The patent replaces the traditional ray marching technique (a computational geometry approach) with a neural network-based implicit representation system. The neural network learns to directly predict depth and normal maps from input images, substituting the mechanical ray marching process with a learned neural mapping that achieves the same reconstruction goal with reduced computational complexity.
Solution Approach 2:
The patent changes the fundamental parameters of the reconstruction approach by using gradient-based optimization to train the neural network on multi-view geometry constraints. This transforms the problem from explicit ray marching with fixed algorithms to a learned parameter optimization problem where the network parameters are adjusted to minimize reconstruction error, enabling more efficient inference.
2Measurement precision
If 2D or 3D plane annotations are used for training, then supervised learning accuracy is improved, but annotation cost increases
Solution Approach 1:
The patent enables the system to self-train by leveraging multi-view geometry constraints from multiple camera poses. Instead of requiring manually annotated 2D or 3D plane data, the system uses the geometric relationships and epipolar constraints inherent in multi-view images to supervise the training process, making the system self-sufficient and eliminating the need for costly annotations.
Solution Approach 2:
The patent introduces multi-view geometry constraints as an intermediary supervisory signal. Rather than using direct plane annotations, the system uses geometric constraints derived from multiple camera views (such as epipolar geometry and fundamental matrices) as a mediator to guide the neural network learning, bridging the gap between unannotated data and supervised learning requirements.
3Reliability
If traditional reconstruction methods are used, then completeness of reconstruction is achieved, but performance degrades with distribution shifts
Solution Approach 1:
The patent makes the reconstruction system dynamic by using a neural network that can adapt to different scenes and camera configurations. The implicit representation learned by the network is flexible and can generalize to new distributions of scenes and imaging conditions, unlike fixed traditional algorithms that degrade when faced with distribution shifts. The system dynamically adjusts its predictions based on the specific input characteristics.
Solution Approach 2:
The patent performs preliminary learning of the scene geometry and surface properties through neural network training before actual reconstruction queries. This preliminary action of learning implicit representations from multi-view data enables the system to handle distribution shifts efficiently, as the network has already internalized the geometric patterns and can generalize to new scenarios without requiring retraining or parameter adjustments.
Data Source
AI summary
Systems and techniques are provided for processing image data corresponding to a scene. A process can include generating a planar distance map including a planar distance value for each pixel of at least one image corresponding to the scene. Planar segmentation is performed based on the planar distance map, a normal map corresponding to the at least one image, and positional encoding information of the planar distance map. A triangular mesh fragment is initialized based on sampling points from each planar segment of a plurality of planar segments from the planar segmentation. Ray-triangle intersections are determined based on performing ray casting for a reconstructed planar mesh including a plurality of triangular mesh fragments each corresponding to a different image. A planar reconstruction and segmentation machine learning network is optimized for the scene, based on training the planar reconstruction and segmentation machine learning network using one or more loss functions.