Skip to main content

Makale: VoxNet: A 3D Convolutional Neural Network for Real-Time Object Recognition

Robust  object  recognition  is  a  crucial  skill  for robots  operating  autonomously  in  real  world  environments. Range  sensors  such  as  LiDAR  and  RGBD  cameras  are  increasingly  found  in  modern  robotic  systems,  providing  a  rich source  of  3D  information  that  can  aid  in  this  task.  However, many current systems do not fully utilize this information and have  trouble  efficiently  dealing  with  large  amounts  of  point cloud  data.  In  this  paper,  we  propose VoxNet,  an  architecture to  tackle  this  problem  by  integrating  a  volumetric  Occupancy Grid representation with a supervised 3D Convolutional Neural Network  (3D  CNN).  We  evaluate  our  approach  on  publicly available  benchmarks  using  LiDAR,  RGBD,  and  CAD  data.
VoxNet achieves  accuracy  beyond  the  state  of  the  art  while labeling hundreds of instances per second.

Ferhat Kurt

Ferhat Kurt

NVIDIA Deep Learning Institute Sertifikalı eğitmen.

Bir Cevap Yazın

This site uses Akismet to reduce spam. Learn how your comment data is processed.

%d blogcu bunu beğendi: