|
| | | | |
| | | |
| Stat |
Members: 3645
Articles: 2'506'133
Articles rated: 2609
26 April 2024 |
|
| | | | |
|
Article overview
| |
|
|
SDOD:Real-time Segmenting and Detecting 3D Objects by Depth | | Caiyi Xu
; Jianping Xing
; Yafei Ning
; Yonghong Chen
; Yong Wu
; | | Date: |
26 Jan 2020 | | Abstract: | Most existing instance segmentation methods only focus on 2D objects and are
not suitable for 3D scenes such as autonomous driving. In this paper, we
propose a model that splits instance segmentation and object detection into two
parallel branches. We discretize the objects depth into "depth categories"
(background set to 0, objects set to [1, K]), then the instance segmentation
task has been transformed into a pixel-level classification task. Mask branch
predicts pixel-level "depth categories", 3D branch predicts instance-level
"depth categories", we produce instance mask by assigning pixels which have
same "depth categories" to each instance. In addition, in order to solve the
problem of imbalanced between mask labels and 3D labels in the KITTI dataset
(200 for mask, 7481 for 3D), we use unreal mask generated by other instance
segmentation method to train mask branch. Despite the use of unreal mask
labels, experiments result on KITTI dataset still achieves state-of-the-art
performance in vehicle instance segmentation. | | Source: | arXiv, 2001.9425 | | Services: | Forum | Review | PDF | Favorites | |
|
|
No review found.
Did you like this article?
Note: answers to reviews or questions about the article must be posted in the forum section.
Authors are not allowed to review their own article. They can use the forum section.
browser Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; ClaudeBot/1.0; +claudebot@anthropic.com)
|
| |
|
|
|
|
News, job offers and information for researchers and scientists:
| |
REGISTER