DensePoint：点群処理のための密な文脈表現学習
DensePoint: Learning Densely Contextual Representation for Efficient Point Cloud Processing
不規則な点群に対応した畳み込み演算子と密結合構造により、多階層・多スケールの文脈情報を効率的に学習する汎用アーキテクチャを提案し、4タスクで最先端性能を達成した。
著者: Yongcheng Liu, Bin Fan, Gaofeng Meng, Jiwen Lu, Shiming Xiang, Chunhong Pan
分類: cs.CV, cs.AI, cs.GR, cs.RO
原文アブストラクト
Point cloud processing is very challenging, as the diverse shapes formed by irregular points are often indistinguishable. A thorough grasp of the elusive shape requires sufficiently contextual semantic information, yet few works devote to this. Here we propose DensePoint, a general architecture to learn densely contextual representation for point cloud processing. Technically, it extends regular grid CNN to irregular point configuration by generalizing a convolution operator, which holds the permutation invariance of points, and achieves efficient inductive learning of local patterns. Architecturally, it finds inspiration from dense connection mode, to repeatedly aggregate multi-level and multi-scale semantics in a deep hierarchy. As a result, densely contextual information along with rich semantics, can be acquired by DensePoint in an organic manner, making it highly effective. Extensive experiments on challenging benchmarks across four tasks, as well as thorough model analysis, verify DensePoint achieves the state of the arts.
