52 research outputs found
TANet: Robust 3D Object Detection from Point Clouds with Triple Attention
In this paper, we focus on exploring the robustness of the 3D object
detection in point clouds, which has been rarely discussed in existing
approaches. We observe two crucial phenomena: 1) the detection accuracy of the
hard objects, e.g., Pedestrians, is unsatisfactory, 2) when adding additional
noise points, the performance of existing approaches decreases rapidly. To
alleviate these problems, a novel TANet is introduced in this paper, which
mainly contains a Triple Attention (TA) module, and a Coarse-to-Fine Regression
(CFR) module. By considering the channel-wise, point-wise and voxel-wise
attention jointly, the TA module enhances the crucial information of the target
while suppresses the unstable cloud points. Besides, the novel stacked TA
further exploits the multi-level feature attention. In addition, the CFR module
boosts the accuracy of localization without excessive computation cost.
Experimental results on the validation set of KITTI dataset demonstrate that,
in the challenging noisy cases, i.e., adding additional random noisy points
around each object,the presented approach goes far beyond state-of-the-art
approaches. Furthermore, for the 3D object detection task of the KITTI
benchmark, our approach ranks the first place on Pedestrian class, by using the
point clouds as the only input. The running speed is around 29 frames per
second.Comment: AAAI 2020(Oral
Giant landslide displacement analysis using a point cloud set conflict technique: a case in Xishancun landslide, Sichuan, China
Landslides, threatening millions of human lives, are geological phenomena on earth, occurred frequently. An increasing number of techniques are being used to monitor landslide deformation. Among th..
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