Efficient Online Surface Correction for Real-time Large-Scale 3D Reconstruction

State-of-the-art methods for large-scale 3D reconstruction from RGB-D sensors usually reduce drift in camera tracking by globally optimizing the estimated camera poses in real-time without simultaneously updating the reconstructed surface on pose changes. We propose an efficient on-the-fly surface correction method for globally consistent dense 3D reconstruction of large-scale scenes. Our approach uses a dense Visual RGB-D SLAM system that estimates the camera motion in real-time on a CPU and refines it in a global pose graph optimization. Consecutive RGB-D frames are locally fused into keyframes, which are incorporated into a sparse voxel hashed Signed Distance Field (SDF) on the GPU. On pose graph updates, the SDF volume is corrected on-the-fly using a novel keyframe re-integration strategy with reduced GPU-host streaming. We demonstrate in an extensive quantitative evaluation that our method is up to 93% more runtime efficient compared to the state-of-the-art and requires significantly less memory, with only negligible loss of surface quality. Overall, our system requires only a single GPU and allows for real-time surface correction of large environments.Comment: British Machine Vision Conference (BMVC), London, September 201

MCCFNet: multi-channel color fusion network for cognitive classification of traditional Chinese paintings.

The computational modeling and analysis of traditional Chinese painting rely heavily on cognitive classification based on visual perception. This approach is crucial for understanding and identifying artworks created by different artists. However, the effective integration of visual perception into artificial intelligence (AI) models remains largely unexplored. Additionally, the classification research of Chinese painting faces certain challenges, such as insufficient investigation into the specific characteristics of painting images for author classification and recognition. To address these issues, we propose a novel framework called multi-channel color fusion network (MCCFNet), which aims to extract visual features from diverse color perspectives. By considering multiple color channels, MCCFNet enhances the ability of AI models to capture intricate details and nuances present in Chinese painting. To improve the performance of the DenseNet model, we introduce a regional weighted pooling (RWP) strategy specifically designed for the DenseNet169 architecture. This strategy enhances the extraction of highly discriminative features. In our experimental evaluation, we comprehensively compared the performance of our proposed MCCFNet model against six state-of-the-art models. The comparison was conducted on a dataset consisting of 2436 TCP samples, derived from the works of 10 renowned Chinese artists. The evaluation metrics employed for performance assessment were Top-1 Accuracy and the area under the curve (AUC). The experimental results have shown that our proposed MCCFNet model significantly outperform all other benchmarking methods with the highest classification accuracy of 98.68%. Meanwhile, the classification accuracy of any deep learning models on TCP can be much improved when adopting our proposed framework

Registration and Fusion of Multi-Spectral Images Using a Novel Edge Descriptor

In this paper we introduce a fully end-to-end approach for multi-spectral image registration and fusion. Our method for fusion combines images from different spectral channels into a single fused image by different approaches for low and high frequency signals. A prerequisite of fusion is a stage of geometric alignment between the spectral bands, commonly referred to as registration. Unfortunately, common methods for image registration of a single spectral channel do not yield reasonable results on images from different modalities. For that end, we introduce a new algorithm for multi-spectral image registration, based on a novel edge descriptor of feature points. Our method achieves an accurate alignment of a level that allows us to further fuse the images. As our experiments show, we produce a high quality of multi-spectral image registration and fusion under many challenging scenarios