3D Point Cloud Depth Completion Using Self-Attention Refinement
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing depth completion techniques rely on computationally complex iterative processing and deformable convolutions, making them unsuitable for resource-constrained devices like smartphones and tablets.
Innovation Solution
Implement depth completion using self-attention based refinement of 3D features, leveraging 3D point clouds and transformer-based layers to enhance depth estimation without iterative refinement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If iterative refinement, deformable convolutions, and dynamic graph propagation are used for depth completion, then depth estimation quality is improved, but computational complexity and device requirements worsen
Solution Approach 1:
The patent extracts and eliminates the iterative refinement step from the traditional depth completion pipeline. By using self-attention mechanisms to directly process 3D point cloud features, the method removes the need for repeated iterative processing while maintaining depth estimation quality, thereby reducing computational complexity
Solution Approach 2:
The patent replaces complex mechanical processing operations (deformable convolutions, dynamic graph propagation) with a self-attention-based neural network approach. This substitution maintains the ability to capture spatial relationships and refine depth features while significantly reducing the computational burden on hardware
2Measurement precision
If iterative refinement methods are used for depth completion, then depth completion quality is improved, but processing time and computational overhead worsen
Solution Approach 1:
The patent performs preliminary processing by converting 2D image features into 3D point cloud features before applying self-attention refinement. This preliminary transformation enables direct processing of spatial information in 3D space, eliminating the need for time-consuming iterative refinement steps while maintaining depth completion accuracy
Solution Approach 2:
The self-attention mechanism continuously refines 3D point cloud features in a single forward pass, maintaining useful computational action throughout the process without the interruptions and repeated cycles of iterative refinement. This continuous processing approach reduces overall processing time while achieving comparable or superior depth completion quality
Data Source
AI summary
Systems and techniques are provided for generating depth information from one or more images. For example, a process can include obtaining a first depth map corresponding to an input comprising an image of the one or more images and a sparse depth measurement. A three-dimensional (3D) point cloud can be generated based on the first depth map and multi-scale visual features of the input, wherein the 3D point cloud includes a plurality of 3D point features uplifted from the multi-scale visual features. At least a portion of the plurality of 3D point features can be processed using one or more self-attention layers to generate refined 3D point features. A two-dimensional (2D) projection of the refined 3D point features can be generated and a second depth map can be generated based on the 2D projection of the refined 3D point features.