Additionally, we conduct auxiliary learning for the point cloud reconstruction model with the point cloud autoencoder via sharing the same knowledge bank. This design enables the knowledge bank to ...

Key players in LiDAR point cloud processing – a disruptive innovation in the automotive industry ‘Application diversity’ measures the number of applications identified for each patent.

Traditional approaches to point cloud completion rely heavily on large-scale datasets with a limited range of shape classes. However, real-world scenarios require the completion of diverse object ...

Second, we proved that learning-based registration depends on the point cloud size. By scaling the point cloud before registration, compared with the best learning-based method before, the accuracy of ...

This work has presented a novel point cloud denoising approach, the visually driven point cloud denoising algorithm (VIPDA). The proposed method differs from other competitors in linking the denoising ...

The choice of design representations, as of search operators, is central to the performance of evolutionary optimization algorithms, in particular, for multitask problems. The multitask approach ...

Junyu-Liu-Nate / Pointnet-Plus-Autoencoder Public forked from yanx27/Pointnet_Pointnet2_pytorch Notifications You must be signed in to change notification settings Fork 0 Star 1 ...

Hi, I am trying to implement autendoer in pytorch and I did write the model which I suppose is excatly what is present in this repo. Model in pytorch ...