Semantic Planar 3D Reconstruction for Depth Hole Repair
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Solution Overview
Problem
Existing 3D reconstruction methods often result in unwanted artifacts due to non-planar surface construction and incomplete depth data, particularly with TOF sensors, which can lead to holes in planar structures and inaccurate virtual object placement.
Innovation Solution
Systems and techniques that incorporate semantically-guided planar constraints to refine depth data by leveraging existing semantic information, smoothing planar surfaces within a TSDF volume, and integrating virtual planes to improve 3D reconstruction accuracy and completeness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional 3D reconstruction methods are used, then the reconstruction process can be completed, but unwanted artifacts are introduced due to non-planar surface construction and incomplete depth data
Solution Approach 1:
The patent applies local quality by differentiating between planar and non-planar surfaces in the 3D reconstruction. Semantic segmentation information is used to identify planar surfaces (such as floors, walls, ceilings) and apply planarity constraints specifically to these regions, while allowing non-planar surfaces to maintain their original depth data without constraints. This localized approach improves reconstruction accuracy for planar surfaces without affecting the overall reconstruction process.
Solution Approach 2:
The patent implements preliminary action by performing semantic segmentation and planar surface detection before the actual 3D reconstruction process. The system pre-identifies which surfaces should be constrained to be planar based on semantic information, and then applies planarity constraints during the reconstruction. This preliminary classification allows the system to target only the necessary regions for constraint application, improving efficiency and accuracy.
2Productivity
If depth data from TOF sensors is used, then the reconstruction can be performed, but holes and inaccuracies are introduced in planar structures
Solution Approach 1:
The patent applies feedback by using semantic segmentation information to guide and correct the depth data processing. The semantic labels (identifying planar surfaces) provide feedback that triggers planarity constraints to be applied to specific regions. This feedback mechanism allows the system to automatically detect and correct holes and inaccuracies in planar structures without requiring manual intervention or complex post-processing.
Solution Approach 2:
The patent uses semantic segmentation maps as an intermediary between the raw depth data and the final 3D reconstruction. The semantic information acts as a mediator that identifies which regions need planarity constraints applied, allowing the system to bridge the gap between incomplete TOF depth data and the desired accurate planar surfaces. This intermediary approach enables correction of depth data inaccuracies without reprocessing the entire reconstruction pipeline.
3Manufacturing precision
If planar constraints are applied to improve surface planarity, then reconstruction quality improves, but computational complexity increases
Solution Approach 1:
The patent reduces processing complexity by applying planar constraints only to locally identified planar surfaces rather than the entire scene. Semantic segmentation information is used to mask and select only the regions that should be constrained (such as floors, walls, ceilings), while non-planar surfaces like objects and furniture are excluded from constraint application. This localized approach significantly reduces the computational burden while maintaining high reconstruction quality for planar surfaces.
Solution Approach 2:
The patent performs preliminary semantic segmentation and planar surface detection before applying any computational constraints. By pre-identifying which surfaces should be constrained based on semantic information, the system avoids unnecessary computational operations on non-planar surfaces. This preliminary classification step enables efficient constraint application by targeting only the necessary regions, reducing overall processing complexity while maintaining high planarity accuracy.
Data Source
AI summary
Systems and techniques are described for image processing. For example, a computing device can generate, based on a first depth map corresponding to a scene and a first semantic segmentation map of the scene, first blocks associated with a planar surface within the scene. The computing device can determine, based on the first blocks, a representation of the planar surface. The computing device can replace, based on the representation of the planar surface, first depths for pixels associated with the planar surface in the first depth map with second depths for the pixels associated with the planar surface from a prior depth map corresponding to the scene to generate an updated depth map corresponding to the scene. The computing device can generate, based on the updated depth map and the first semantic segmentation map, second blocks associated with the planar surface within the scene.