(2)利用CNN进行特征提取,这里的网络包括两个:Depth CNN学习深度图上的特征,RGB CNN学习2D图像上的特征,最后利用SVM进行分类。 在对Depth Map的利用上,论文所述方法并没有直接利用CNN对其进行学习,而是encode the depth image with three channels at each pixel: horizontal disparity(水平视差), height above groun...
(2)利用CNN进行特征提取,这里的网络包括两个:Depth CNN学习深度图上的特征,RGB CNN学习2D图像上的特征,最后利用SVM进行分类。 在对Depth Map的利用上,论文所述方法并没有直接利用CNN对其进行学习,而是encode the depth image with three channels at each pixel: horizontal disparity(水平视差), height above groun...
[16] Geiger A, Wang C. Joint 3D object and layout inference from a single RGB-D image. Proceedings of German Conference on Pattern Recognition, 2015: 183-195. [17] Guo YX, Tong X. View-volume network for semantic scene completion from a single depth image. Proceedings of the Internationa...
We address the problem of inferring the pose of an RGB-D camera relative to a known 3D scene, given only a single acquired image. Our approach employs a regression forest that is capable of inferring an estimate of each pixel’s correspondence to 3D points in the scene’s world coordinate ...
题目: End-to-End RGB-D Image Compression via Exploiting Channel-Modality Redundancy 作者: Huiming Zheng, Wei Gao 论文链接: https://underline.io/lecture/92675-end-to-end-rgb-d-image-compression-via-exploiting-channel-modality-redundancy 来源:AAAI 2024 内容整理:周楚骎 RGB-D图像作为一种3D数据,已...
Superpixel Based RGB-D Image Segmentation Using Markov Random Field——阅读笔记 1、基本信息 题目:使用马尔科夫场实现基于超像素的RGB-D图像分割; 作者所属:Ferdowsi University of Mashhad(Iron) 发表:2015 International Symposium on Artificial Intelligence and Signal Processing (AISP)...
Similar advances in RGB-D image understanding are hampered by the current lack of large labeled datasets in this domain. We have a developed a new technique "cross-modal distillation" which enables us to transfer supervision from RGB to RGB-D datasets. We use representations learned from ...
RRN用于学习在RGB图像和thermal image间的映射,而后学习得到的模型就可以用于依据RGB生成thermal image。RRN接收RGB+行人proposals,在ROI Pooling后加了重建网络(全卷积)。这里的重建网络不重建整幅图像的thermal image,而是只对行人区域进行重建。 (2)Multi-Scale Detection Network (MSDN) ...
Benchmarking Self-Supervised Contrastive Learning Methods for Image-Based Plant Phenotyping https://doi.org/10.34133/plantphenomics.0037 Plant Phenomics | 基于图像的植物表型自监督对比学习方法的基准测试 Multispectral Drone Imagery and SRGAN for Rapid Phenotypic Mapping of Individual Chinese Cabbage Plants ...
3d object modeling and recognition using local affine-invariant image descriptors and multi-view spatial constraints. DeepIM: Deep Iterative Matching for 6D Pose Estimation - Yi Li, Gu Wang, Xiangyang Ji, Yu Xiang, Dieter Fox. [Paper] Stacked hourglass networks for human pose estimation Making de...