港中文自動駕駛點雲上取樣方法

重磅乾貨，第一時間送達

Abstract

Point clouds acquired from range scans are often sparse， noisy， and non-uniform。 This paper presents a new point cloud upsampling network called PU-GAN， which is formulated based on a generative adversarial network （GAN）， to learn a rich variety of point distributions from the latent space and upsample points over patches on object surfaces。 To realize a working GAN network， we construct an up-down-up expansion unit in the generator for upsampling point features with error feedback and self-correction， andformulate a self-attention unit to enhance the feature integration。 Further， we design a compound loss with adversarial， uniform and reconstruction terms， to encourage the discriminator to learn more latent patterns and enhance theoutput point distribution uniformity。 Qualitative and quantitative evaluations demonstrate the quality of our results over the state-of-the-arts in terms of distribution uniformity， proximity-to-surface， and 3D reconstruction quality。

從距離掃描獲得的點雲通常是稀疏的、有噪聲的和不均勻的。提出了一種基於生成對抗網路（GAN）的點雲上取樣網路PU-GAN，用於從目標表面的潛在空間和斑塊上的上取樣點學習豐富的點分佈。為了實現一個工作的GAN網路，我們在發生器中構造了一個上下向上擴充套件單元，用於誤差反饋和自校正的上取樣點特徵，並構造了一個自關注單元以增強特徵的整合度。進一步，我們設計了一個具有對抗性、一致性和重構項的複合損耗，以鼓勵鑑別器學習更多的潛在模式，提高輸出點分佈的均勻性。定性和定量評估表明，我們的結果在藝術狀態的分佈均勻性，接近表面，和三維重建質量的質量。