Hybrid Scene Reconstruction for Real-Time 3D Depth Refinement
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Solution Overview
Problem
Existing methods for three-dimensional scene reconstruction from input image data are hindered by increased runtime and memory requirements, leading to latency issues in augmented reality applications and agent navigation.
Innovation Solution
A hybrid scene reconstruction model that generates a raw heightfield from predicted depth maps and refines it using feature maps, blending the two to achieve high-quality, real-time scene reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If high-quality scene reconstruction methods are used, then reconstruction quality is improved, but runtime and memory requirements increase causing latency
Solution Approach 1:
The reconstruction process is segmented into two distinct components: a raw heightfield generation stage that processes visible surfaces quickly, and a refinement stage that selectively improves occluded areas. This segmentation allows the system to achieve high-quality reconstruction without processing the entire scene at maximum detail, thereby reducing overall runtime latency while maintaining reconstruction quality.
Solution Approach 2:
The system applies different quality levels to different regions of the scene. Visible surfaces are reconstructed with standard quality using rapid processing, while occluded areas receive enhanced refinement processing. This local quality approach ensures high reconstruction quality where needed without uniformly increasing computational requirements across the entire scene, thus addressing the runtime-latency tradeoff.
2Manufacturing precision
If high-quality scene reconstruction methods are used, then reconstruction quality is improved, but memory requirements increase
Solution Approach 1:
Memory usage is segmented by separating the raw heightfield data structure from the refinement processing. The raw heightfield uses compact memory representation for visible surfaces, while refinement features are computed and applied selectively only to occluded areas. This segmentation prevents the entire scene from requiring high-detail memory allocation simultaneously, reducing overall memory requirements while maintaining reconstruction quality.
Solution Approach 2:
The system allocates computational resources and memory locally to where they are most needed - specifically in occluded areas that require refinement. Rather than maintaining high-detail representations throughout the entire scene, memory is concentrated on problematic regions, achieving high reconstruction quality where necessary without proportionally increasing total memory requirements.
3Productivity
If delayed scene reconstruction occurs, then computational resources are reduced, but navigational guidance determination is delayed
Solution Approach 1:
The system performs preliminary reconstruction of the raw heightfield from visible surfaces before refinement is needed. This preliminary action provides immediate navigational guidance based on visible terrain, while refinement of occluded areas proceeds in parallel or subsequently. This ensures that navigational guidance is not delayed, as the essential visible terrain information is available immediately without waiting for complete scene refinement.
Solution Approach 2:
The system extracts and prioritizes the essential navigational information from visible surfaces, separating it from the refinement of occluded areas. This extraction allows navigational guidance to be determined immediately from the raw heightfield data, while computational resources can be reduced during the optional refinement phase, thus maintaining productivity without delaying critical navigational functions.
Data Source
AI summary
A computer-implemented method is disclosed for generating scene reconstructions from image data. The method includes: receiving image data of a scene captured by a camera; inputting the image data of the scene into a scene reconstruction model; receiving, from the scene reconstruction model, a final spatial model of the scene, wherein the scene reconstruction model generates the final spatial model by: predicting a depth map for each image of the image data, extracting a feature map for each image of the image data, generating a first spatial model based on the predicted depth maps of the images, generating a second spatial model based on the extracted feature maps of the images, and determining the final spatial model by combining the first spatial model and the second spatial model; and providing functionality on a computing device related to the scene and based on the final spatial model.