DFusion: Denoised TSDF Fusion of Multiple Depth Maps with Sensor Pose Noises.
Zhaofeng Niu1, Yuichiro Fujimoto1, Masayuki Kanbara1
1Nara Institute of Science and Technology (NAIST), Ikoma 630-0192, Nara, Japan.
Sensors (Basel, Switzerland)
|February 26, 2022
Summary
DFusion minimizes sensor noise in 3D reconstruction by fusing depth maps and denoising truncated signed distance function (TSDF) volumes. This novel approach effectively addresses both depth and pose noises for improved accuracy.
Area of Science:
- Computer Vision
- Robotics
- 3D Reconstruction
Background:
- Truncated signed distance function (TSDF) fusion is crucial for 3D reconstruction.
- Existing methods are susceptible to sensor noise, impacting reconstruction quality.
Purpose of the Study:
- To introduce DFusion, a novel network for robust TSDF fusion.
- To mitigate the effects of depth and pose noises in 3D reconstruction.
Main Methods:
- DFusion employs a fusion module to generate TSDF volumes from depth maps.
- A denoising module with 3D convolutional layers processes TSDF volumes to remove noise.
- A specialized loss function enhances fusion in object and surface areas.
Main Results:
- DFusion effectively reduces the impact of depth and pose noises.
- Experimental results on synthetic and real-world datasets demonstrate superior performance.
- The method shows improved fusion accuracy in critical object and surface regions.
Conclusions:
- DFusion presents a pioneering solution for simultaneous depth and pose noise reduction in TSDF fusion.
- The proposed network significantly enhances the robustness and accuracy of 3D reconstruction.
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