Recently, neural implicit function-basedrepresentation has attracted more and more attention,and has been widely used to represent surfacesusing differentiable neural networks. However, surfacereconstruction from poin...Recently, neural implicit function-basedrepresentation has attracted more and more attention,and has been widely used to represent surfacesusing differentiable neural networks. However, surfacereconstruction from point clouds or multi-view imagesusing existing neural geometry representations stillsuffer from slow computation and poor accuracy. Toalleviate these issues, we propose a multi-scale hashencoding-based neural geometry representation whicheffectively and efficiently represents the surface asa signed distance field. Our novel neural networkstructure carefully combines low-frequency Fourierposition encoding with multi-scale hash encoding. Theinitialization of the geometry network and geometryfeatures of the rendering module are accordinglyredesigned. Our experiments demonstrate that theproposed representation is at least 10 times faster forreconstructing point clouds with millions of points.It also significantly improves speed and accuracyof multi-view reconstruction. Our code and modelsare available at https://github.com/Dengzhi-USTC/Neural-Geometry-Reconstruction.展开更多
We developed a novel absolute multi-pole encoder structure to improve the resolution of the multi-pole encoder, realize absolute output and reduce the manufacturing cost of the encoder. The structure includes two ring...We developed a novel absolute multi-pole encoder structure to improve the resolution of the multi-pole encoder, realize absolute output and reduce the manufacturing cost of the encoder. The structure includes two ring alnicos defined as index track and sub-division track, respectively. The index track is magnetized based on the improved gray code, with linear halls placed around the track evenly. The outputs of linear halls show the region the rotor belongs to. The sub-division track is magnetized to N-S-N-S (north-south-north-south), and the number of N-S pole pairs is determined by the index track. Three linear hall sensors with an air-gap of 2 mm are used to translate the magnetic filed to voltage signals. The relative offset in a single N-S is obtained through look-up. The magnetic encoder is calibrated using a higher-resolution incremental optical encoder. The pulse output from the optical encoder and hall signals from the magnetic encoder are sampled at the same time and transmitted to a computer, and the relation between them is calculated, and stored in the FLASH of MCU (micro controller unit) for look-up. In the working state, the absolute angle is derived by looking-up with hall signals. The structure is simple and the manufacturing cost is very low and suitable for mass production.展开更多
基金supported by the National Natural Science Foundation of China(Nos.62122071 and 62272433)the Fundamental Research Funds for the Central Universities(No.WK3470000021)the Alibaba Innovation Research Program(AIR).
文摘Recently, neural implicit function-basedrepresentation has attracted more and more attention,and has been widely used to represent surfacesusing differentiable neural networks. However, surfacereconstruction from point clouds or multi-view imagesusing existing neural geometry representations stillsuffer from slow computation and poor accuracy. Toalleviate these issues, we propose a multi-scale hashencoding-based neural geometry representation whicheffectively and efficiently represents the surface asa signed distance field. Our novel neural networkstructure carefully combines low-frequency Fourierposition encoding with multi-scale hash encoding. Theinitialization of the geometry network and geometryfeatures of the rendering module are accordinglyredesigned. Our experiments demonstrate that theproposed representation is at least 10 times faster forreconstructing point clouds with millions of points.It also significantly improves speed and accuracyof multi-view reconstruction. Our code and modelsare available at https://github.com/Dengzhi-USTC/Neural-Geometry-Reconstruction.
基金Funded partly by Heilongjiang Province Financial Fund for Researchers Returning from Abroad
文摘We developed a novel absolute multi-pole encoder structure to improve the resolution of the multi-pole encoder, realize absolute output and reduce the manufacturing cost of the encoder. The structure includes two ring alnicos defined as index track and sub-division track, respectively. The index track is magnetized based on the improved gray code, with linear halls placed around the track evenly. The outputs of linear halls show the region the rotor belongs to. The sub-division track is magnetized to N-S-N-S (north-south-north-south), and the number of N-S pole pairs is determined by the index track. Three linear hall sensors with an air-gap of 2 mm are used to translate the magnetic filed to voltage signals. The relative offset in a single N-S is obtained through look-up. The magnetic encoder is calibrated using a higher-resolution incremental optical encoder. The pulse output from the optical encoder and hall signals from the magnetic encoder are sampled at the same time and transmitted to a computer, and the relation between them is calculated, and stored in the FLASH of MCU (micro controller unit) for look-up. In the working state, the absolute angle is derived by looking-up with hall signals. The structure is simple and the manufacturing cost is very low and suitable for mass production.
