Add task-specific repository data formats

README.md

@@ -91,6 +91,20 @@ Evaluation follows point-cloud geometry metrics after back-projecting predicted
 - Chamfer Distance (CD, meters).
 - Recall under temporal-bin tolerances of `1`, `3`, and `5` bins.
 
+#### Repository data format
+
+```text
+DepthEstimate/
+  Histgram/
+    1.txt ... 10.txt
+  gt/
+    1.csv ... 10.csv
+```
+
+- `DepthEstimate/Histgram/{id}.txt` stores the SPAD transient input for one depth-estimation sample. Each non-empty line is a whitespace-separated photon-count vector with `672` temporal bins. The valid pixel rows correspond to the flattened `256 x 192` SPL image grid.
+- `DepthEstimate/gt/{id}.csv` stores the corresponding Livox reference point cloud. The CSV header includes `Timestamp`, metric coordinates `X,Y,Z`, `Reflectivity`, and original Livox fields such as `Ori_x,Ori_y,Ori_z`.
+- Matching sample ids are used across `Histgram/` and `gt/`, e.g. `Histgram/1.txt` pairs with `gt/1.csv`.
+
 ### Task 2: Multi-view 3D Reconstruction
 
 ![Multi-view reconstruction qualitative results](fig/Multi-view%20reconstruction.png)
@@ -115,6 +129,29 @@ Reported metrics cover three output domains:
 - Depth rendering: per-pixel `L1` error over valid pixels.
 - Histogram rendering: PSNR for methods that explicitly render transient histograms.
 
+#### Repository data format
+
+```text
+Reconstruction/
+  AI_floor2.zip
+  artbuilding_floor2.zip
+  c4floor2.zip
+  design_floor1.zip
+  library_floor2.zip
+  material_building.zip
+  parking.zip
+  physics_building2.zip
+  physics_building3.zip
+
+config/
+  config.yaml
+```
+
+- Each file under `Reconstruction/` is one compressed scene package for multi-view reconstruction.
+- The scene package contains the per-view transient observations and the metadata needed by the reconstruction benchmark for that location.
+- `config/config.yaml` provides common sensor settings and SPL calibration parameters used when parsing the released data, including `dt_ps: 750`, `image_sizes: [256, 192]`, and calibrated intrinsics/distortion parameters for `p1` and `p2`.
+- Benchmark experiments select `3`, `5`, or `10` views from a scene package as input views and reserve the remaining views for novel-view evaluation.
+
 ### Task 3: 3D Semantic Segmentation
 
 ![Semantic segmentation qualitative results](fig/Semantic%20Segmentation.png)
@@ -140,6 +177,34 @@ The semantic subset contains `10,297` frames captured across `27` sequences and
 
 The benchmark uses `13` semantic classes. Evaluation reports Overall Accuracy (OA) and mean Intersection-over-Union (mIoU), averaged over three random seeds in the paper protocol.
 
+#### Repository data format
+
+```text
+Annotations/
+  p1/
+    Sequence1.zip ... Sequence20.zip
+  p2/
+    Sequence21.zip ... Sequence27.zip
+
+Histgram/
+  p1.zip
+  p2.zip
+  depth_maps/
+    P1_Sequence1_depth.png ... P1_Sequence20_depth.png
+    P2_Sequence21_depth.png ... P2_Sequence27_depth.png
+
+config/
+  config.yaml
+```
+
+- `Annotations/p1/` contains semantic annotation packages for sequences captured by SPL device `p1`.
+- `Annotations/p2/` contains semantic annotation packages for sequences captured by SPL device `p2`.
+- Inside each sequence zip, semantic labels are stored as `.npy` arrays named like `RawDataHistogramMap_frame_*_semantic.npy`.
+- Each semantic array has shape `(49152, 672)`, where `49152 = 256 x 192` flattened pixels and `672` is the number of temporal bins. Values are `uint8` semantic ids, with `0` used for unlabeled/background bins and `1-13` for semantic classes.
+- `Histgram/p1.zip` and `Histgram/p2.zip` store the corresponding raw histogram data for the semantic sequences.
+- `Histgram/depth_maps/` provides sequence-level depth-map PNG files aligned with the `P1_Sequence*` and `P2_Sequence*` naming convention.
+- `config/config.yaml` contains the calibrated intrinsics for both SPL devices. The semantic track uses both `p1` and `p2`, while the depth and reconstruction tracks use the relevant device calibration required by their data.
+
 ## Semantic Annotation Format
 
 ![Annotation pipeline](fig/annotation_pipeline.png)