@@ -5,6 +5,7 @@
     # network
     trt_precision: fp16
     cloud_capacity: 2000000
+    source_reconstruction: full
     onnx_path: "$(var model_path)/ptv3.onnx"
     engine_path: "$(var model_path)/ptv3.engine"
     filter:
 
@@ -35,12 +35,23 @@ class PostprocessCuda
 
   void createVisualizationPointcloud(
     const float * input_features, const std::int64_t * pred_labels, float * output_points,
-    std::size_t num_points);
+    std::size_t num_classes, std::size_t num_points);
 
   void createSegmentationPointcloud(
     const float * input_features, const std::int64_t * pred_labels, const float * pred_probs,
     std::uint8_t * output_points, std::size_t num_classes, std::size_t num_points);
 
+  void reconstructPartial(
+    const std::int64_t * inverse_map, const std::int64_t * voxel_labels, const float * voxel_probs,
+    std::int64_t * output_labels, float * output_probs, std::size_t num_classes,
+    std::size_t num_cropped_points, std::size_t num_voxels);
+
+  void reconstructFull(
+    const std::uint32_t * crop_mask, const std::uint32_t * crop_indices,
+    const std::int64_t * inverse_map, const std::int64_t * voxel_labels, const float * voxel_probs,
+    std::int64_t * output_labels, float * output_probs, std::size_t num_classes,
+    std::size_t num_points, std::size_t num_voxels);
+
   std::size_t createFilteredPointcloud(
     const void * compact_input_points, CloudFormat input_format, CloudFormat output_format,
     const float * pred_probs, void * output_points, std::size_t num_classes,
 
@@ -35,7 +35,11 @@ class PreprocessCuda
   std::size_t generateFeatures(
     const void * input_data, CloudFormat input_format, unsigned int num_points,
     float * voxel_features, std::int64_t * voxel_coords, std::int64_t * voxel_hashes,
-    void * compact_points);
+    void * compact_points, float * reconstruction_features, void * cropped_source_points,
+    std::int64_t * inverse_map, std::size_t * num_cropped_points);
+
+  [[nodiscard]] const std::uint32_t * cropMask() const { return crop_mask_d_.get(); }
+  [[nodiscard]] const std::uint32_t * cropIndices() const { return crop_indices_d_.get(); }
 
 private:
   PTv3Config config_;
 
@@ -28,6 +28,12 @@
 namespace autoware::ptv3
 {
 
+enum class SourceReconstruction {
+  NONE,
+  PARTIAL,
+  FULL,
+};
+
 class PTv3Config
 {
 public:
@@ -36,7 +42,8 @@ class PTv3Config
     const std::vector<std::int64_t> & voxels_num, const std::vector<float> & point_cloud_range,
     const std::vector<float> & voxel_size, const std::vector<std::string> & class_names,
     const std::vector<std::int64_t> & palette, const float filter_class_probability_threshold,
-    const std::vector<std::string> & filter_classes, const std::string & filter_output_format)
+    const std::vector<std::string> & filter_classes, const std::string & filter_output_format,
+    const std::string & source_reconstruction)
   {
     plugins_path_ = plugins_path;
 
@@ -91,6 +98,21 @@ class PTv3Config
     filter_class_probability_threshold_ = filter_class_probability_threshold;
     filter_class_indices_ = make_filter_class_indices(class_names_, filter_classes);
     filter_output_format_ = filter_output_format;
+    source_reconstruction_ = parse_source_reconstruction(source_reconstruction);
+  }
+
+  static SourceReconstruction parse_source_reconstruction(const std::string & value)
+  {
+    if (value == "none") {
+      return SourceReconstruction::NONE;
+    }
+    if (value == "partial") {
+      return SourceReconstruction::PARTIAL;
+    }
+    if (value == "full") {
+      return SourceReconstruction::FULL;
+    }
+    throw std::runtime_error("source_reconstruction must be one of: 'none', 'partial', or 'full'.");
   }
 
   static std::vector<std::uint32_t> make_filter_class_indices(
@@ -155,6 +177,7 @@ class PTv3Config
   float filter_class_probability_threshold_{};
   std::vector<std::uint32_t> filter_class_indices_;
   std::string filter_output_format_;
+  SourceReconstruction source_reconstruction_{SourceReconstruction::NONE};
 
   // Common network parameters
   std::int64_t cloud_capacity_{};
 
@@ -100,8 +100,16 @@ class PTV3_PUBLIC PTv3TRT
 
   // Preprocess outputs
   std::int64_t num_voxels_{0};
+  std::int64_t num_cropped_points_{0};        // only for partial
+  std::int64_t num_source_points_{0};         // only for full
+  const void * current_input_data_{nullptr};  // only for full
 
   CudaUniquePtr<std::uint8_t[]> compact_points_d_{nullptr};
+  CudaUniquePtr<std::uint8_t[]> cropped_source_points_d_{nullptr};  // only for partial
+  CudaUniquePtr<float[]> reconstructed_features_d_{nullptr};        // only for partial and full
+  CudaUniquePtr<std::int64_t[]> inverse_map_d_{nullptr};            // only for partial and full
+  CudaUniquePtr<std::int64_t[]> reconstructed_labels_d_{nullptr};   // only for partial and full
+  CudaUniquePtr<float[]> reconstructed_probs_d_{nullptr};           // only for partial and full
   CudaUniquePtr<std::int64_t[]> grid_coord_d_{nullptr};
   CudaUniquePtr<float[]> feat_d_{nullptr};
   CudaUniquePtr<std::int64_t[]> serialized_code_d_{nullptr};
 
@@ -40,13 +40,13 @@
   <group unless="$(var use_pointcloud_container)">
     <node pkg="autoware_ptv3" exec="autoware_ptv3_node" name="ptv3" output="screen" args="--ros-args --log-level $(var log_level)">
       <remap from="~/input/pointcloud" to="$(var input/pointcloud)"/>
-      <remap from="~/input/pointcloud/cuda" to="$(var input/pointcloud)/cuda"/>
+      <remap from="~/input/pointcloud/cuda" to="$(var input/pointcloud)/cuda/dummy"/>
       <remap from="~/output/pointcloud/segmentation" to="$(var output/pointcloud/segmentation)"/>
-      <remap from="~/output/pointcloud/segmentation/cuda" to="$(var output/pointcloud/segmentation)/cuda"/>
+      <remap from="~/output/pointcloud/segmentation/cuda" to="$(var output/pointcloud/segmentation)/cuda/dummy"/>
       <remap from="~/output/pointcloud/visualization" to="$(var output/pointcloud/visualization)"/>
-      <remap from="~/output/pointcloud/visualization/cuda" to="$(var output/pointcloud/visualization)/cuda"/>
+      <remap from="~/output/pointcloud/visualization/cuda" to="$(var output/pointcloud/visualization)/cuda/dummy"/>
       <remap from="~/output/pointcloud/filtered" to="$(var output/pointcloud/filtered)"/>
-      <remap from="~/output/pointcloud/filtered/cuda" to="$(var output/pointcloud/filtered)/cuda"/>
+      <remap from="~/output/pointcloud/filtered/cuda" to="$(var output/pointcloud/filtered)/cuda/dummy"/>
 
       <param from="$(var model_param_path)" allow_substs="true"/>
       <param from="$(var ml_package_param_path)" allow_substs="true"/>
 
@@ -26,19 +26,21 @@ struct OutputSegmentationPointType
   float z;
   std::uint8_t class_id;
   float probability;
+  float entropy;
 } __attribute__((packed));
 
 __global__ void createVisualizationPointcloudKernel(
   const float4 * input_features, const float * colors, const std::int64_t * labels,
-  float4 * output_points, std::size_t num_points)
+  float4 * output_points, std::size_t num_classes, std::size_t num_points)
 {
   const auto idx = static_cast<std::uint32_t>(blockIdx.x * blockDim.x + threadIdx.x);
   if (idx >= num_points) {
     return;
   }
 
   const auto label = labels[idx];
-  const auto color = colors[label];
+  const auto color =
+    label >= 0 && static_cast<std::size_t>(label) < num_classes ? colors[label] : 0.0f;
 
   output_points[idx] =
     make_float4(input_features[idx].x, input_features[idx].y, input_features[idx].z, color);
@@ -55,11 +57,78 @@ __global__ void createSegmentationPointcloudKernel(
 
   const auto input_point = input_features[idx];
   const auto label = labels[idx];
+  const bool has_valid_label = label >= 0 && static_cast<std::size_t>(label) < num_classes;
+  float entropy = 0.0f;
+  for (std::size_t class_idx = 0; class_idx < num_classes; ++class_idx) {
+    const auto probability = pred_probs[idx * num_classes + class_idx];
+    if (probability > 0.0f) {
+      entropy -= probability * logf(probability);
+    }
+  }
+  if (num_classes > 1) {
+    entropy /= logf(static_cast<float>(num_classes));
+  }
   output_points[idx].x = input_point.x;
   output_points[idx].y = input_point.y;
   output_points[idx].z = input_point.z;
-  output_points[idx].class_id = static_cast<std::uint8_t>(label);
-  output_points[idx].probability = pred_probs[idx * num_classes + label];
+  output_points[idx].class_id = has_valid_label ? static_cast<std::uint8_t>(label) : 255U;
+  output_points[idx].probability = has_valid_label ? pred_probs[idx * num_classes + label] : 0.0f;
+  output_points[idx].entropy = entropy;
+}
+
+__global__ void reconstructPartialKernel(
+  const std::int64_t * __restrict__ inverse_map, const std::int64_t * __restrict__ voxel_labels,
+  const float * __restrict__ voxel_probs, std::int64_t * __restrict__ output_labels,
+  float * __restrict__ output_probs, std::size_t num_classes, std::size_t num_cropped_points,
+  std::size_t num_voxels)
+{
+  const auto point_idx = blockIdx.x * blockDim.y + threadIdx.y;
+  const auto class_idx = blockIdx.y * blockDim.x + threadIdx.x;
+
+  if (point_idx >= num_cropped_points || class_idx >= num_classes) return;
+
+  const auto voxel_idx = inverse_map[point_idx];
+  const bool has_valid_voxel = voxel_idx >= 0 && static_cast<std::size_t>(voxel_idx) < num_voxels;
+  if (class_idx == 0) {
+    output_labels[point_idx] = has_valid_voxel ? voxel_labels[voxel_idx] : 255;
+  }
+
+  output_probs[point_idx * num_classes + class_idx] =
+    has_valid_voxel ? voxel_probs[static_cast<std::size_t>(voxel_idx) * num_classes + class_idx]
+                    : 0.0f;
+}
+
+__global__ void reconstructFullKernel(
+  const std::uint32_t * __restrict__ crop_mask, const std::uint32_t * __restrict__ crop_indices,
+  const std::int64_t * __restrict__ inverse_map, const std::int64_t * __restrict__ voxel_labels,
+  const float * __restrict__ voxel_probs, std::int64_t * __restrict__ output_labels,
+  float * __restrict__ output_probs, std::size_t num_classes, std::size_t num_points,
+  std::size_t num_voxels)
+{
+  const auto point_idx = blockIdx.x * blockDim.y + threadIdx.y;
+  const auto class_idx = blockIdx.y * blockDim.x + threadIdx.x;
+
+  if (point_idx >= num_points || class_idx >= num_classes) return;
+
+  const auto mask = crop_mask[point_idx];
+  if (mask == 0) {
+    if (class_idx == 0) output_labels[point_idx] = 255;
+    return;
+  }
+
+  const auto cropped_idx = crop_indices[point_idx] - 1;
+  const auto voxel_idx = inverse_map[cropped_idx];
+  if (voxel_idx < 0 || static_cast<std::size_t>(voxel_idx) >= num_voxels) {
+    if (class_idx == 0) output_labels[point_idx] = 255;
+    return;
+  }
+
+  if (class_idx == 0) {
+    output_labels[point_idx] = voxel_labels[voxel_idx];
+  }
+
+  output_probs[point_idx * num_classes + class_idx] =
+    voxel_probs[static_cast<std::size_t>(voxel_idx) * num_classes + class_idx];
 }
 
 template <typename OutputPointT>
@@ -213,13 +282,13 @@ PostprocessCuda::PostprocessCuda(const PTv3Config & config, cudaStream_t stream)
 
 void PostprocessCuda::createVisualizationPointcloud(
   const float * input_features, const std::int64_t * labels, float * output_points,
-  std::size_t num_points)
+  std::size_t num_classes, std::size_t num_points)
 {
   auto num_blocks = divup(num_points, config_.threads_per_block_);
 
   createVisualizationPointcloudKernel<<<num_blocks, config_.threads_per_block_, 0, stream_>>>(
     reinterpret_cast<const float4 *>(input_features), color_map_d_.get(), labels,
-    reinterpret_cast<float4 *>(output_points), num_points);
+    reinterpret_cast<float4 *>(output_points), num_classes, num_points);
 
   CHECK_CUDA_ERROR(cudaStreamSynchronize(stream_));
 }
@@ -237,6 +306,37 @@ void PostprocessCuda::createSegmentationPointcloud(
   CHECK_CUDA_ERROR(cudaStreamSynchronize(stream_));
 }
 
+void PostprocessCuda::reconstructPartial(
+  const std::int64_t * inverse_map, const std::int64_t * voxel_labels, const float * voxel_probs,
+  std::int64_t * output_labels, float * output_probs, std::size_t num_classes,
+  std::size_t num_cropped_points, std::size_t num_voxels)
+{
+  auto block = dim3(32, 8);
+  auto grid = dim3(divup(num_cropped_points, block.y), divup(num_classes, block.x));
+
+  reconstructPartialKernel<<<grid, block, 0, stream_>>>(
+    inverse_map, voxel_labels, voxel_probs, output_labels, output_probs, num_classes,
+    num_cropped_points, num_voxels);
+
+  CHECK_CUDA_ERROR(cudaStreamSynchronize(stream_));
+}
+
+void PostprocessCuda::reconstructFull(
+  const std::uint32_t * crop_mask, const std::uint32_t * crop_indices,
+  const std::int64_t * inverse_map, const std::int64_t * voxel_labels, const float * voxel_probs,
+  std::int64_t * output_labels, float * output_probs, std::size_t num_classes,
+  std::size_t num_points, std::size_t num_voxels)
+{
+  auto block = dim3(32, 8);
+  auto grid = dim3(divup(num_points, block.y), divup(num_classes, block.x));
+
+  reconstructFullKernel<<<grid, block, 0, stream_>>>(
+    crop_mask, crop_indices, inverse_map, voxel_labels, voxel_probs, output_labels, output_probs,
+    num_classes, num_points, num_voxels);
+
+  CHECK_CUDA_ERROR(cudaStreamSynchronize(stream_));
+}
+
 std::size_t PostprocessCuda::createFilteredPointcloud(
   const void * compact_input_points, CloudFormat input_format, CloudFormat output_format,
   const float * pred_probs, void * output_points, std::size_t num_classes, std::size_t num_points)