研究目的
To address the 3D reconstruction problem for dynamic non-rigid objects with a single RGB-D sensor, focusing on mitigating accumulation errors and surface tracking failures in long sequences.
研究成果
The proposed framework effectively reconstructs 3D shapes and appearances of deformable objects by addressing drifting through loop closure and utilizing both geometric and color constraints. It achieves accurate and complete models with high-fidelity color maps, as validated on synthetic and real datasets. Future work includes improving appearance with textures, handling changing topologies, and enhancing robustness to fast motion.
研究不足
The method relies on the success of building partial scans, which may fail with fast motion. Topology is assumed constant, and it does not handle changing topologies. The approach is offline and not real-time, taking about 40 minutes for processing. Occluded parts may not be fully reconstructed, and color filling for unobserved regions is not addressed.
1：Experimental Design and Method Selection:
The methodology involves a global non-rigid registration framework with explicit loop closure. It includes partitioning the input RGB-D sequence into segments, fusing frames to create partial scans, pairwise non-rigid registration using the Embedded Deformation Model, loop detection via SIFT features, global registration with loop constraints, and a model-update step for refinement. Both geometric and color information are utilized for alignment.
2：Sample Selection and Data Sources:
Synthetic datasets generated from textured mesh models and real datasets captured using a Microsoft Kinect V2 sensor with human subjects rotating in front of it.
3：List of Experimental Equipment and Materials:
Microsoft Kinect V2 sensor for data capture, desktop computer with 8-core 3.6 GHz Intel CPU and 16 GB memory for processing, and MATLAB 2016a software for implementation.
4：6 GHz Intel CPU and 16 GB memory for processing, and MATLAB 2016a software for implementation.
Experimental Procedures and Operational Workflow:
4. Experimental Procedures and Operational Workflow: The pipeline includes partial pieces generation, pairwise registration, loop detection, global registration, and model update. Parameters are set with specific weights (e.g., αr=100, αs=1000) and optimized using the Levenberg-Marquardt algorithm.
5：Data Analysis Methods:
Quantitative evaluation involves computing geometric errors compared to ground truth models, and qualitative assessment through visual inspection of reconstructed models and color maps.
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