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pro vyhledávání: '"Indoor Scene Understanding"'
Autor:
Wang, Yonghui, Chen, Shi-Yong, Zhou, Zhenxing, Li, Siyi, Li, Haoran, Zhou, Wengang, Li, Houqiang
Recently, Vision Language Models (VLMs) have experienced significant advancements, yet these models still face challenges in spatial hierarchical reasoning within indoor scenes. In this study, we introduce ROOT, a VLM-based system designed to enhance
Externí odkaz:
http://arxiv.org/abs/2411.15714
Data diversity and abundance are essential for improving the performance and generalization of models in natural language processing and 2D vision. However, 3D vision domain suffers from the lack of 3D data, and simply combining multiple 3D datasets
Externí odkaz:
http://arxiv.org/abs/2402.14215
This paper proposes a shape anchor guided learning strategy (AncLearn) for robust holistic indoor scene understanding. We observe that the search space constructed by current methods for proposal feature grouping and instance point sampling often int
Externí odkaz:
http://arxiv.org/abs/2309.11133
The volume and diversity of training data are critical for modern deep learningbased methods. Compared to the massive amount of labeled perspective images, 360 panoramic images fall short in both volume and diversity. In this paper, we propose PanoMi
Externí odkaz:
http://arxiv.org/abs/2309.09514
Autor:
Yang, Yu-Qi, Guo, Yu-Xiao, Xiong, Jian-Yu, Liu, Yang, Pan, Hao, Wang, Peng-Shuai, Tong, Xin, Guo, Baining
The use of pretrained backbones with fine-tuning has been successful for 2D vision and natural language processing tasks, showing advantages over task-specific networks. In this work, we introduce a pretrained 3D backbone, called {\SST}, for 3D indoo
Externí odkaz:
http://arxiv.org/abs/2304.06906
6D object pose estimation problem has been extensively studied in the field of Computer Vision and Robotics. It has wide range of applications such as robot manipulation, augmented reality, and 3D scene understanding. With the advent of Deep Learning
Externí odkaz:
http://arxiv.org/abs/2212.01920
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Autor:
Tang, Shengjun, Huang, Hongsheng, Zhang, Yunjie, Yao, Mengmeng, Li, Xiaoming, Xie, Linfu, Wang, Weixi
Publikováno v:
In Automation in Construction December 2023 156
Autor:
Baruch, Gilad, Chen, Zhuoyuan, Dehghan, Afshin, Dimry, Tal, Feigin, Yuri, Fu, Peter, Gebauer, Thomas, Joffe, Brandon, Kurz, Daniel, Schwartz, Arik, Shulman, Elad
Scene understanding is an active research area. Commercial depth sensors, such as Kinect, have enabled the release of several RGB-D datasets over the past few years which spawned novel methods in 3D scene understanding. More recently with the launch
Externí odkaz:
http://arxiv.org/abs/2111.08897
Publikováno v:
In Information Fusion June 2023 94:32-42