3D Scene Reconstruction via Camera Pose Clustering
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Solution Overview
Problem
Conventional 3D scene representations, such as those using neural radiance fields (NeRFs), suffer from reduced detail and image quality when rendering large-scale scenes due to high detail only near the center point, leading to blurry images when viewed from farther distances.
Innovation Solution
The method involves clustering images based on camera poses to generate separate 3D representations for each cluster, allowing for higher resolution near the center and progressively lower resolution further away, enabling more detailed and clear rendering of large-scale scenes by assigning each image to clusters based on camera pose and generating distinct 3D representations for each cluster.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single 3D representation is used for the entire scene, then the structure is simple, but the detail and image quality deteriorate when viewed from far from the center point
Solution Approach 1:
The patent divides the scene into multiple regions based on camera pose clustering, creating multiple 3D representations (NeRFs) instead of a single monolithic representation. Each cluster of images generates a dedicated 3D representation focused on that specific region, allowing high detail throughout the entire scene rather than sacrificing peripheral detail for structural simplicity.
Solution Approach 2:
Each 3D representation is generated with high detail for its specific local region corresponding to the clustered images. The patent ensures that every region of the scene receives a dedicated 3D representation optimized for that local area, providing high detail quality locally while maintaining overall scene coverage through the collection of regional representations.
2Area of stationary object
If images far from center point are used to train a single NeRF, then the field of view is expanded, but the detail in those regions deteriorates due to large cone coverage
Solution Approach 1:
The patent segments the field of view into multiple regional 3D representations, each handling a specific portion of the scene. Instead of one NeRF trying to cover the entire field of view and sacrificing peripheral detail, multiple smaller NeRFs each cover their respective regions with high detail, collectively achieving comprehensive field of view without detail loss.
3Device complexity
If conventional NeRF training is applied to large-scale scenes, then the method is simple, but the rendering time and processing requirements increase significantly
Solution Approach 1:
The patent segments the training process into parallel tasks, where each cluster of images is processed independently to generate a separate 3D representation. This segmentation enables parallel processing across multiple computational units, significantly reducing total rendering time while maintaining the simplicity of the overall approach through automated clustering and parallel execution.
4Device complexity
If a single 3D representation is used for large-scale scenes, then the data structure is simple, but the resolution away from center point is reduced
Solution Approach 1:
The patent segments the scene into multiple regions, each with its own 3D representation that maintains high resolution. Instead of one data structure that must compromise peripheral resolution, multiple data structures each maintain high resolution for their respective regions, collectively providing uniform high resolution across the entire large-scale scene.
Data Source
AI summary
One embodiment of a method for generating representations of scenes includes assigning each image included in a set of images of a scene to one or more clusters of images based on a camera pose associated with the image, and performing one or more operations to generate, for each cluster included in the one or more clusters, a corresponding three-dimensional (3D) representation of the scene based on one or more images assigned to the cluster.