attr/m46188: decimation based LoD generation

This LoD generation (subsampling) method uses the Morton-based ordering
of points that is used to determine prediction neighbours.  Each LoD
level comprises 3/4 of the points of the previous.

cfg/octree-liftt-ctc-lossless-geom-lossy-attrs.yaml

@@ -33,9 +33,10 @@ categories:
      - positionQuantizationScaleAdjustsDist2: 1
      - dist2: ${seq_dist2}
      - lodBinaryTree: 0
+     - lodDecimation: 0
      -
        - !conditional '"${group}" =~ m{^cat3}'
-       - lodBinaryTree: 1
+       - lodDecimation: 1
 
      ##
      # attribute coding -- reflectance

cfg/octree-liftt-ctc-lossy-geom-lossy-attrs.yaml

@@ -42,9 +42,10 @@ categories:
        - positionQuantizationScaleAdjustsDist2: 1
        - dist2: ${seq_dist2}
        - lodBinaryTree: 0
+       - lodDecimation: 0
        -
          - !conditional '"${group}" =~ m{^cat3}'
-         - lodBinaryTree: 1
+         - lodDecimation: 1
 
        ##
        # attribute coding -- reflectance

cfg/trisoup-liftt-ctc-lossy-geom-lossy-attrs.yaml

@@ -33,9 +33,10 @@ categories:
      - numberOfNearestNeighborsInPrediction: 3
      - levelOfDetailCount: ${seq_lod}
      - lodBinaryTree: 0
+     - lodDecimation: 0
      -
        - !conditional '"${group}" =~ m{^cat3}'
-       - lodBinaryTree: 1
+       - lodDecimation: 1
      - positionQuantizationScaleAdjustsDist2: 1
      - dist2: ${seq_dist2}
 

doc/README.options.md

@@ -254,6 +254,14 @@ Range for nearest neighbour search.
 Maximum number of nearest neighbour candidates used in direct
 attribute prediction.
 
+### `--lodDecimation=0|1`
+Controls the level-of-detail generation method:
+
+  | Value | Description                     |
+  |:-----:| ------------------------------- |
+  | 0     | Euclidean distance thresholding |
+  | 1     | Decimation by 1:3               |
+
 ### `--levelOfDetailCount=INT-VALUE`
 Attribute's number of levels of detail.
 

tmc3/AttributeDecoder.cpp

@@ -277,9 +277,10 @@ AttributeDecoder::decodeReflectancesPred(
         predictors, indexesLOD);
     } else {
       buildPredictorsFast(
-        pointCloud, aps.dist2, aps.num_detail_levels,
-        aps.num_pred_nearest_neighbours, aps.search_range, aps.search_range,
-        predictors, numberOfPointsPerLOD, indexesLOD);
+        pointCloud, aps.lod_decimation_enabled_flag, aps.dist2,
+        aps.num_detail_levels, aps.num_pred_nearest_neighbours,
+        aps.search_range, aps.search_range, predictors, numberOfPointsPerLOD,
+        indexesLOD);
     }
   } else {
     buildLevelOfDetailBinaryTree(pointCloud, numberOfPointsPerLOD, indexesLOD);
@@ -373,9 +374,10 @@ AttributeDecoder::decodeColorsPred(
         predictors, indexesLOD);
     } else {
       buildPredictorsFast(
-        pointCloud, aps.dist2, aps.num_detail_levels,
-        aps.num_pred_nearest_neighbours, aps.search_range, aps.search_range,
-        predictors, numberOfPointsPerLOD, indexesLOD);
+        pointCloud, aps.lod_decimation_enabled_flag, aps.dist2,
+        aps.num_detail_levels, aps.num_pred_nearest_neighbours,
+        aps.search_range, aps.search_range, predictors, numberOfPointsPerLOD,
+        indexesLOD);
     }
   } else {
     buildLevelOfDetailBinaryTree(pointCloud, numberOfPointsPerLOD, indexesLOD);
@@ -576,9 +578,9 @@ AttributeDecoder::decodeColorsLift(
 
   if (!aps.lod_binary_tree_enabled_flag) {
     buildPredictorsFast(
-      pointCloud, aps.dist2, aps.num_detail_levels,
-      aps.num_pred_nearest_neighbours, aps.search_range, aps.search_range,
-      predictors, numberOfPointsPerLOD, indexesLOD);
+      pointCloud, aps.lod_decimation_enabled_flag, aps.dist2,
+      aps.num_detail_levels, aps.num_pred_nearest_neighbours, aps.search_range,
+      aps.search_range, predictors, numberOfPointsPerLOD, indexesLOD);
   } else {
     buildLevelOfDetailBinaryTree(pointCloud, numberOfPointsPerLOD, indexesLOD);
     computePredictors(
@@ -659,9 +661,9 @@ AttributeDecoder::decodeReflectancesLift(
 
   if (!aps.lod_binary_tree_enabled_flag) {
     buildPredictorsFast(
-      pointCloud, aps.dist2, aps.num_detail_levels,
-      aps.num_pred_nearest_neighbours, aps.search_range, aps.search_range,
-      predictors, numberOfPointsPerLOD, indexesLOD);
+      pointCloud, aps.lod_decimation_enabled_flag, aps.dist2,
+      aps.num_detail_levels, aps.num_pred_nearest_neighbours, aps.search_range,
+      aps.search_range, predictors, numberOfPointsPerLOD, indexesLOD);
   } else {
     buildLevelOfDetailBinaryTree(pointCloud, numberOfPointsPerLOD, indexesLOD);
     computePredictors(

tmc3/AttributeEncoder.cpp

@@ -482,9 +482,10 @@ AttributeEncoder::encodeReflectancesPred(
         predictors, indexesLOD);
     } else {
       buildPredictorsFast(
-        pointCloud, aps.dist2, aps.num_detail_levels,
-        aps.num_pred_nearest_neighbours, aps.search_range, aps.search_range,
-        predictors, numberOfPointsPerLOD, indexesLOD);
+        pointCloud, aps.lod_decimation_enabled_flag, aps.dist2,
+        aps.num_detail_levels, aps.num_pred_nearest_neighbours,
+        aps.search_range, aps.search_range, predictors, numberOfPointsPerLOD,
+        indexesLOD);
     }
   } else {
     buildLevelOfDetailBinaryTree(pointCloud, numberOfPointsPerLOD, indexesLOD);
@@ -679,9 +680,10 @@ AttributeEncoder::encodeColorsPred(
         predictors, indexesLOD);
     } else {
       buildPredictorsFast(
-        pointCloud, aps.dist2, aps.num_detail_levels,
-        aps.num_pred_nearest_neighbours, aps.search_range, aps.search_range,
-        predictors, numberOfPointsPerLOD, indexesLOD);
+        pointCloud, aps.lod_decimation_enabled_flag, aps.dist2,
+        aps.num_detail_levels, aps.num_pred_nearest_neighbours,
+        aps.search_range, aps.search_range, predictors, numberOfPointsPerLOD,
+        indexesLOD);
     }
   } else {
     buildLevelOfDetailBinaryTree(pointCloud, numberOfPointsPerLOD, indexesLOD);
@@ -969,9 +971,9 @@ AttributeEncoder::encodeColorsLift(
 
   if (!aps.lod_binary_tree_enabled_flag) {
     buildPredictorsFast(
-      pointCloud, aps.dist2, aps.num_detail_levels,
-      aps.num_pred_nearest_neighbours, aps.search_range, aps.search_range,
-      predictors, numberOfPointsPerLOD, indexesLOD);
+      pointCloud, aps.lod_decimation_enabled_flag, aps.dist2,
+      aps.num_detail_levels, aps.num_pred_nearest_neighbours, aps.search_range,
+      aps.search_range, predictors, numberOfPointsPerLOD, indexesLOD);
   } else {
     buildLevelOfDetailBinaryTree(pointCloud, numberOfPointsPerLOD, indexesLOD);
     computePredictors(
@@ -1074,9 +1076,9 @@ AttributeEncoder::encodeReflectancesLift(
 
   if (!aps.lod_binary_tree_enabled_flag) {
     buildPredictorsFast(
-      pointCloud, aps.dist2, aps.num_detail_levels,
-      aps.num_pred_nearest_neighbours, aps.search_range, aps.search_range,
-      predictors, numberOfPointsPerLOD, indexesLOD);
+      pointCloud, aps.lod_decimation_enabled_flag, aps.dist2,
+      aps.num_detail_levels, aps.num_pred_nearest_neighbours, aps.search_range,
+      aps.search_range, predictors, numberOfPointsPerLOD, indexesLOD);
   } else {
     buildLevelOfDetailBinaryTree(pointCloud, numberOfPointsPerLOD, indexesLOD);
     computePredictors(

tmc3/PCCTMC3Common.h

@@ -579,7 +579,7 @@ computeNearestNeighbors(
 //---------------------------------------------------------------------------
 
 inline void
-subsample(
+subsampleByDistance(
   const PCCPointSet3& pointCloud,
   const std::vector<MortonCodeWithIndex>& packedVoxel,
   const std::vector<uint32_t>& input,
@@ -613,6 +613,44 @@ subsample(
 
 //---------------------------------------------------------------------------
 
+inline void
+subsampleByDecimation(
+  const std::vector<uint32_t>& input,
+  std::vector<uint32_t>& retained,
+  std::vector<uint32_t>& indexes)
+{
+  static const int kLodUniformQuant = 4;
+  const int indexCount = int(input.size());
+  for (int i = 0; i < indexCount; ++i) {
+    if (i % kLodUniformQuant == 0)
+      retained.push_back(input[i]);
+    else
+      indexes.push_back(input[i]);
+  }
+}
+
+//---------------------------------------------------------------------------
+
+inline void
+subsample(
+  bool useDecimation,
+  const PCCPointSet3& pointCloud,
+  const std::vector<MortonCodeWithIndex>& packedVoxel,
+  const std::vector<uint32_t>& input,
+  const double radius2,
+  const int32_t searchRange,
+  std::vector<uint32_t>& retained,
+  std::vector<uint32_t>& indexes)
+{
+  if (useDecimation)
+    subsampleByDecimation(input, retained, indexes);
+  else
+    subsampleByDistance(
+      pointCloud, packedVoxel, input, radius2, searchRange, retained, indexes);
+}
+
+//---------------------------------------------------------------------------
+
 inline void
 computeMortonCodes(
   const PCCPointSet3& pointCloud,
@@ -799,6 +837,7 @@ computePredictors(
 inline void
 buildPredictorsFast(
   const PCCPointSet3& pointCloud,
+  bool lod_decimation_enabled_flag,
   const std::vector<int64_t>& dist2,
   const int32_t levelOfDetailCount,
   const int32_t numberOfNearestNeighborsInPrediction,
@@ -843,8 +882,8 @@ buildPredictorsFast(
     } else {
       const double radius2 = dist2[lodIndex];
       subsample(
-        pointCloud, packedVoxel, input, radius2, searchRange1, retained,
-        indexes);
+        lod_decimation_enabled_flag, pointCloud, packedVoxel, input, radius2,
+        searchRange1, retained, indexes);
     }
     const int32_t endIndex = indexes.size();
 

tmc3/TMC3.cpp

@@ -430,6 +430,12 @@ ParseParameters(int argc, char* argv[], Parameters& params)
     " 0: distance based subsampling\n"
     " 1: binary tree")
 
+  ("lodDecimation",
+    params_attr.aps.lod_decimation_enabled_flag, false,
+    "Controls LoD generation method:\n"
+    " 0: distance based subsampling\n"
+    " 1: decimation by 1:3")
+
   ("max_num_direct_predictors",
     params_attr.aps.max_num_direct_predictors, 3,
     "Maximum number of nearest neighbour candidates used in direct"

tmc3/hls.h

@@ -233,6 +233,7 @@ struct AttributeParameterSet {
   //--- lifting/predicting transform parameters
 
   bool lod_binary_tree_enabled_flag;
+  bool lod_decimation_enabled_flag;
   int num_pred_nearest_neighbours;
   int max_num_direct_predictors;
   int adaptive_prediction_threshold;

tmc3/io_hls.cpp

@@ -262,6 +262,7 @@ write(const AttributeParameterSet& aps)
     bs.writeUe(aps.num_pred_nearest_neighbours);
     bs.writeUe(aps.max_num_direct_predictors);
     bs.writeUe(aps.search_range);
+    bs.write(aps.lod_decimation_enabled_flag);
     bs.write(aps.lod_binary_tree_enabled_flag);
 
     bs.writeUe(aps.num_detail_levels);
@@ -311,6 +312,7 @@ parseAps(const PayloadBuffer& buf)
     bs.readUe(&aps.num_pred_nearest_neighbours);
     bs.readUe(&aps.max_num_direct_predictors);
     bs.readUe(&aps.search_range);
+    bs.read(&aps.lod_decimation_enabled_flag);
     bs.read(&aps.lod_binary_tree_enabled_flag);
 
     aps.num_detail_levels = int(bs.readUe());