Semantic segmentation based on the complementary information from RGB and depth images has recently gained great popularity, but due to the difference between RGB and depth maps, how to effectively use RGB-D information is still a problem. In this paper, we propose a novel RGB-D semantic ...
Example: To apply ESANet-R34-NBt1D trained on SUNRGB-D to samples from SUNRGB-D, run: #note that the entire first batch is visualized, so larger batch sizes results#in smaller images in the plotpython inference_dataset.py \ --dataset sunrgbd \ --dataset_dir ./datasets/sunrgbd \ --ck...
代码:https://github.com/VCIP-RGBD/DFormer这个工作来自南开大学。观点很有意思,当前的RGBD语义分割方法一般会分别使用CNN或者Transformer的编码器提取 RGB 和 深度图 的特征,作者认为 深度可以被视为RGB图像…
【论文笔记(2)】3D Graph Neural Networks for RGBD Semantic Segmentation,程序员大本营,技术文章内容聚合第一站。
Extensive experiments witness the effectiveness of the CANet in fusing RGB and depth features, achieving state-of-the-art performance on two challenging RGB-D semantic segmentation datasets, i.e., NYUDv2 and SUN-RGBD. Introduction Semantic segmentation, which aims at assigning each pixel into ...
3D Graph Neural Networks for RGBD Semantic Segmentation 原文章:https://www.yuque.com/lart/papers/wmu47a 动机 主要针对的任务是RGBD语义分割, 不同于往常的RGB图像的语义分割任务, 这里还可以更多的考虑来自D通道的深度信息. 所以对于这类任务需要联合2D外观和3D几何信息来进行联合推理. ...
SUNRGBD:由4个RGB-D传感器采集得到,包干10000张图片,规模类似于PASCAL VOC数据集,整个数据集被密集地标注了,包括多边形,带有方向的边框,以及三维空间的布局和类别,适合于场景理解任务。 The Object Segmentation Database (OSD):这个数据库的设计是为了将未知对象从一般的场景中分割出来,即使是在部分遮挡的情况下。类...
STD2P: RGBD Semantic Segmentation Using Spatio-Temporal Data-Driven Pooling Yang He, Wei-Chen Chiu, Margret Keuper, Mario Fritz STD2P:使用时空数据驱动池化的RGBD语义分割 Abstract:我们提出了一种新的基于超像素的多视图(multi-view)卷积神经网络用于语义图像分割。所提出的网络通过利用来自相同场景的(additiona...
These leaderboards are used to track progress in Robust Semi-Supervised RGBD Semantic Segmentation TrendDatasetBest ModelPaperCodeCompare 2D-3D-S M3L (Linear Fusion B2) See all SUN RGB-D M3L (Linear Fusion B2) See all Datasets SUN RGB-D 2D-3D-S Most implemented papers Most impl...
Dataset NYUv2 SUN-RGBD Method RGB D-CNN Z-ACN RGB D-CNN Z-ACN PixelAcc (%) 50.1 60.3 73.5 66.6 72.4 78.4 mIoU (%) 15.9 27.8 28.4 22.8 29.7 30.5Table II: Quantitative comparison with VGG-16 based methods on NYUv2 dataset. Our method significantly boosts the performance over the baselin...