IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2015 – Paper

Tim Golla, Reinhard Klein – Uni Bonn – “Real-time Point Cloud Compression” – With  today’s  advanced  3D  scanner  technology, huge  amounts  of  point  cloud  data  can  be  generated  in  short amounts of time. Data compression is thus necessary for storage and  especially  for  transmission,  e.g.,  via  wireless  networks. While  previous  approaches  delivered  good  compression  ratios and interesting theoretical insights, they are either computationally  expensive  or  do  not  support  incrementally  acquired  data and locally decompressing the data, two requirements we found necessary  in  many  applications.  We  present  a  compression approach that is efficient in storage requirements as well as in computational  cost,  as  it  can  compress  and  decompress  point cloud  data  in  real-time.  Furthermore,  it  is  capable  of  compressing incrementally acquired data, local decompression and of  decompressing  a  subsampled  representation  of  the  original data.  Our  method  is  based  on  local  2D  parameterizations  of surface  point  cloud  data,  for  which  we  describe  an  efficient approach. We suggest the usage of standard image compression techniques for the compression of local details. While exhibiting state-of-the-art compression ratios, our approach remains easy to implement. In our evaluation, we compare our approach to previous ones and discuss the choice of parameters. Due to our algorithm’s efficiency, we consider it as a reference concerning speed and compression rates. (PDF)