PointRCNN阅读笔记

最新推荐文章于 2024-05-20 13:57:34 发布

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最新推荐文章于 2024-05-20 13:57:34 发布 · 554 阅读

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#深度学习

Instead of generating proposals from RGB image or projecting point cloud to bird's view or voxels as previous methods do, our stage-1 sub-network directly generates a small number of high-quality 3D proposals from point cloud in a bottom-up manner via segmenting the point cloud of the whole scene into foreground points and background.

第一阶段把点云分割成前景和背景，而不是从RGB、BEV或Voxel生成proposals。

用点云分割自下而上地生成3D proposal

AVOD places 80-100k anchor boxes in the 3D space and pool features for each anchor in multiple views for generating proposals. F-PointNet generates 2D proposals from 2D images, and estimate 3D boxes based on the 3D points cropped from the 2D regions, which might miss difficult objects that could only be clearly observed from 3D space.

作者认为，AVOD和F-PointNet都有各自的缺点。AVOD在3D空间设置了80-100k个anchor，每个anchor利用不同视图的信息提取proposals（估计很占显存）；F-PointNet用2D图片生成2D proposal，基于从2D裁剪出的3D点估计3D box，可能错过只能从3D空间观察到的难样本。

作者提出，在第一阶段用全场景点云分割来提取3D proposal。基于3D场景下物体不重叠的事实，3D GT box里面的点就是分割mask，即前景点。

We learn point-wise features to segment the raw point cloud and to generate 3D proposals from the segmented foreground points simutaneously.

Our method avoids using a large set of predefined 3D boxes in the 3D space and significantly constrains the search space for 3D proposal generation.

做法：把点云分割，分割后前景点就是proposals。这样避免使用大量预定义的3D boxes，限制了3D proposals的搜索空间。