Ashish Patel 🇮🇳’s Post

𝗗𝗮𝘆-𝟮𝟱𝟵 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗣𝗼𝗶𝗻𝘁𝗠𝗮𝗻𝗶𝗳𝗼𝗹𝗱𝗖𝘂𝘁: Point-wise Augmentation in the Manifold for Point Clouds by University of Science and Technology, Wuhan, Hubei Follow me for a similar post: 🇮🇳 Ashish Patel Interesting Facts : 🔸 This paper is published arxiv 2021. ------------------------------------------------------------------- 𝗔𝗺𝗮𝘇𝗶𝗻𝗴 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 : https://lnkd.in/gJ59RxBU Code: https://lnkd.in/g4mKd-Ei ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 🔸 Augmentation can benefit point cloud learning due to the limited availability of large-scale public datasets. 🔸This paper proposes a mix-up augmentation approach, PointManifoldCut, which replaces the neural network embedded points, rather than the Euclidean space coordinates. 🔸This approach takes the advantage that points at the higher levels of the neural network are already trained to embed its neighbors relations and mixing these representation will not mingle the relation between itself and its label. 🔸This allows to regularize the parameter space as the other augmentation methods but without worrying about the proper label of the replaced points. 🔸The experiments show that our proposed approach provides a competitive performance on point cloud classification and segmentation when it is combined with the cutting-edge vanilla point cloud networks. 🔸The result shows a consistent performance boosting compared to other state-of-the-art point cloud augmentation method, such as PointMixup and PointCutMix. #computervision #artificialintelligence #machinelearning