1. 24 Apr, 2019 1 commit
    • David Flynn's avatar
      geom/m47957: update kNeighPattern64to6 for N9|5|3|2 operation · 83a062f5
      David Flynn authored
      Maintains contexts for:
       64->6:    0n, ud, 1n, 2nh, 2nv, 3n
       64->6->5: 0n, ud+1n, 2nh, 2nv, 3n
       64->6->3: 0n, ud+1n+2nh, 2nv+3n
      Where ud = up/down,
            1n = single neighbour
            2nh = two horizontal neighbours
            2nv = two vertical neighbours
            3n = three neighbours
            0n = no neighbours
  2. 16 Apr, 2019 30 commits
  3. 15 Apr, 2019 1 commit
  4. 25 Feb, 2019 2 commits
  5. 11 Feb, 2019 1 commit
  6. 08 Feb, 2019 1 commit
  7. 06 Feb, 2019 4 commits
    • David Flynn's avatar
      release: update version to 5.0-rc1 · 551d2a84
      David Flynn authored
    • Yiting Shao's avatar
      slice/m44910: add octree slice partitioning scheme · 08a4a4e9
      Yiting Shao authored and David Flynn's avatar David Flynn committed
      This partitioning method (--partitionMethod=3) decomposes the input
      pointcloud into an octree of depth --partitionOctreeDepth=d, with
      each leaf node corresponding to a slice.
    • Yiting Shao's avatar
      slice/m44910: add longest-edge slice partitioning scheme · 81bde409
      Yiting Shao authored and David Flynn's avatar David Flynn committed
      This partitioning method (--partitionMethod=2) finds the longest edge of
      the point cloud and divides it into --partitionNumUniformGeom=n slices
      along the longest edge.
      If n = 0, the ratio of longest edge to shortest edge determines
      the number of slices.
    • David Flynn's avatar
      hls: initial slice and partitioning framework · 693f8517
      David Flynn authored
      This commit provides a basic frame work for partitioning a frame into
      multiple slices, with the continued assumption of single-frame
      The decoder is modified to independently decode each slice and
      accumulate decoded points in a buffer for output.
      The encoder is updated to support partitioning the input point cloud
      into slices and to independently code each slice.  Points from
      reconstructed slices are accumulated and output at the end of the
      frame period.
      The partitioning process (partitioning methods are defined in
      partitioning.cpp) proceeds as follows:
       - quantise the input point cloud without duplicate point removal
         or reordering points.
       - apply the partitioning function to produce a list of tiles
         and slices, each slice having an origin, id, and list of point
         indexes that identify points in the input point cloud.
       - producing a source point cloud for each partition as a subset
         of the input point cloud.
       - compressing each partition (slice) as normal by quantising
         the partitioned input.  Recolouring is necessarily performed
         against the partitioned input since the recolouring method
         cannot correctly handle recolouring a partition from a complete
         point cloud.
      NB: this commit does not provide any partitioning methods.