In complex$360^{\circ }$scenes, depth estimation is challenging for small objects and the depth of object boundaries, which cannot be effectively solved with existing works.$360^{\circ }$depth estimation is unable to produce uniform depth estimate findings in both indoor and outdoor settings due to the datasets. In this paper, the Useg-PanoDepth and PanoDepth dataset is proposed to improve the above problems effectively. The Diagonal-aware Attention Module (DAM) effectively estimates small objects in complex scenes. Enhanced Boundary Module (EBM), for enhancing boundary information,can also effectively solve the problem of depth unification of indoor and outdoor scenes. Extensive experiments on our constructed PanoDepth dataset, Useg-PanoDepth achieves SOTA results. The Relative accuracy (deltahttps://github.com/xjh6/Useg-PanoDepth.

We describe a method for 3D object scanning by aligning depth scans that were taken from around an object with a Time-of-Flight (ToF) camera. These ToF cameras can measure depth scans at video rate. Due to comparably simple technology, they bear potential for economical production in big volumes. Our easy-to-use, cost-effective scanning solution, which is based on such a sensor, could make 3D scanning technology more accessible to everyday users. The algorithmic challenge we face is that the sensor's level of random noise is substantial and there is a nontrivial systematic bias. In this paper, we show the surprising result that 3D scans of reasonable quality can also be obtained with a sensor of such low data quality. Established filtering and scan alignment techniques from the literature fail to achieve this goal. In contrast, our algorithm is based on a new combination of a 3D superresolution method with a probabilistic scan alignment approach that explicitly takes into account the sensor's noise characteristics.

We describe a method for 3D object scanning by aligning depth scans that were taken from around an object with a time-of-flight camera. These ToF cameras can measure depth scans at video rate. Due to comparably simple technology they bear potential for low cost production in big volumes. Our easy-to-use, cost-effective scanning solution based on such a sensor could make 3D scanning technology more accessible to everyday users. The algorithmic challenge we face is that the sensor's level of random noise is substantial and there is a non-trivial systematic bias. In this paper we show the surprising result that 3D scans of reasonable quality can also be obtained with a sensor of such low data quality. Established filtering and scan alignment techniques from the literature fail to achieve this goal. In contrast, our algorithm is based on a new combination of a 3D superresolution method with a probabilistic scan alignment approach that explicitly takes into account the sensor's noise characteristics.

Scale Invariant Feature Transform (SIFT) has been proven to be the most robust local invariant feature descriptor. However, SIFT is designed mainly for grayscale images. Many local features can be misclassified if their color information is ignored. Motivated by perceptual principles, this paper addresses a new color space, called perception-based color space, in which the associated metric approximates perceived distances and color displacements and captures illumination invariant relationship. Instead of using grayscale values to represent the input image, the proposed approach builds the SIFT descriptors in the new color space, resulting in a descriptor that is more robust than the standard SIFT with respect to color and illumination variations. The evaluation results support the potential of the proposed approach.