Indoor modeling based on structured-light camera and implicit surface reconstruction
Depth cameras are highly valued in indoor modeling for their rapidness,precision,and robustness.Yet,they encounter challenges such as environmental light interference,point cloud noise,and incomplete data depth.To overcome these hurdles,an innovative structured light neural implicit surface(SL-NeuS)reconstruction method integrates a broad spectrum of 3D reconstruction technologies,including multi-view stereo(MVS),neural networks,neural radiance fields,and neural implicit surfaces(NeuS).This method leverages the differentiable recurrent optimization-inspired design simultaneous localization and mapping(DROID-SLAM)algorithm to accurately determine camera extrinsics.By incorporating monocular depth estimation and monocular normal estimation of prior information,it achieves precise indoor 3D modeling.Experimental findings demonstrate that the SL-NeuS reconstruction method excels in performance analysis across diverse depth cameras,effectively minimizing the impact of environmental light and optical distortions on modeling accuracy.Applying this method to model various indoor environments keeps errors within a narrow range,ensuring high-precision indoor 3D reconstruction.In addition to enhancing indoor modeling precision,it significantly reduces training time,offering essential technical support for digital architecture and digital navigation fields.
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维普
