3D Reconstruction of Dense Model based on the Sparse Frames using RGBD Camera With the popularity of consumergrade depth cameras on mobile devices, 3D reconstruction based on the RGBD camera has once again become a hot topic in the field of 3D vision. The current application of RGBD cameras is mainly in the field of largescale 3D reconstruction and VSLAM that rely on highperformance graphics cards to achieve realtime reconstruction and currently reach a relatively mature stage. The reconstruction of specific scene models ( such as objects, human bodies, human faces ) has a lot of application prospects in the fields of intelligent volume measurement, face recognition, motion capture, etc, attracting the attention of researchers. In this paper, a lightweight 3D reconstruction algorithm framework is designed for scene objects. The selecting frame module is introduced. The dense 3D reconstruction based on sparse frames is implemented on the Raspberry Pi 4B to improve the model reconstruction efficiency and we design the point cloud poseprocessing module to improve the quality of the model reconstruction.

RGBtoTSDF: Direct TSDF Prediction from a Single RGB Image for Dense 3D Reconstruction In this paper, we present a novel method to predict 3D TSDF voxels from a single image for dense 3D reconstruction. 3D reconstruction with RGB images has two inherent problems: scale ambiguity and sparse reconstruction. With the advent of deep learning, depth prediction from a single RGB image has addressed these problems. However, as the predicted depth is typically noisy, denoising methods such as TSDF fusion should be adapted for the accurate scene reconstruction. To integrate the twostep processing of depth prediction and TSDF generation, we design an RGBtoTSDF network to directly predict 3D TSDF voxels from a single RGB image. The TSDF using our network can be generated more efficiently in terms of time and accuracy than the TSDF converted from depth prediction. We also use the predicted TSDF for a more accurate and robust camera pose estimation to complete scene reconstruction. The global TSDF is updated from TSDF prediction and pose estimation, and thus dense isosurface can be extracted. In the experiments, we evaluate our TSDF prediction and camera pose estimation results against the conventional method.