Update dataset card and figures

README.md

@@ -1,58 +1,198 @@
 ---
-license: mit
+license: cc-by-nc-sa-4.0
+task_categories:
+- depth-estimation
+- image-to-3d
+- image-segmentation
+tags:
+- single-photon-lidar
+- spad
+- transient-imaging
+- time-of-flight
+- 3d-vision
+- multi-view-reconstruction
+- semantic-segmentation
 pretty_name: SP-TransientBench
+size_categories:
+- 10K<n<100K
 ---
 
 # SP-TransientBench
 
-SP-TransientBench is a single-photon transient perception dataset. This repository currently contains annotation files, histogram data, reconstruction data, and depth-estimation files.
+**SP-TransientBench (STB)** is a real-captured single-photon LiDAR benchmark for photon-starved 3D perception. It provides full per-pixel time-of-flight histograms, calibrated metadata, and task-specific supervision for depth estimation, multi-view 3D reconstruction, and 3D semantic segmentation.
 
-## Repository Structure
+![SP-TransientBench overview](fig/overview.png)
+
+## Highlights
+
+- Real-captured SPAD transient benchmark collected with an Adaps ADS6311 solid-state single-photon LiDAR.
+- Full waveform data for each view: `256 x 192 x 672` time-resolved photon histograms.
+- Three benchmark tracks: depth estimation, multi-view 3D reconstruction, and 3D semantic segmentation.
+- Task-specific calibration, reference geometry, poses, semantic labels, illumination metadata, and pile-up metadata.
+- Full release size: approximately `168.7 GB`.
+
+## Sensor Setup
+
+![SPL hardware setup](fig/SPL.jpg)
+
+STB is captured with a flash single-photon LiDAR system based on Direct Time of Flight (DToF) and Time-Correlated Single Photon Counting (TCSPC). The transmitter uses a 940 nm VCSEL array, and the receiver records photon arrival timestamps with a SPAD array.
+
+| Item | Value |
+| --- | --- |
+| SPL device | Adaps ADS6311 Hawk |
+| Acquisition mode | Solid-state flash SPL |
+| Raw SPAD resolution | `768 x 576` |
+| Released output resolution | `256 x 192` after `3 x 3` on-chip binning |
+| Histogram bins | `672` |
+| Bin width | `750 ps` |
+| Field of view | `128 deg x 96 deg` |
+| Frame rate | `10-20 Hz` |
+| Detection range | Up to `30 m` |
+| Range accuracy | `< 5 cm` |
+
+An auxiliary Livox Avia LiDAR is mounted with the SPL device during collection. It is used for pose estimation, SPL-Livox calibration, and depth-reference generation where required by the benchmark track.
+
+## Dataset Overview
+
+![Dataset statistics](fig/dataset%20statistics.png)
+
+The current release contains three complementary subsets:
+
+| Track | Scale | Main purpose |
+| --- | ---: | --- |
+| Depth Estimation | `10` samples | Single-view depth recovery from raw transient histograms |
+| Multi-view 3D Reconstruction | `10` scenes, `20-40` transient views per scene | Scene reconstruction and novel-view evaluation from calibrated SPL views |
+| 3D Semantic Segmentation | `27` sequences, `10,297` frames | Semantic understanding from SPAD-derived geometry |
+
+Every released view contains the same core sensing data:
+
+- Raw SPAD histogram: full waveform tensor with shape `256 x 192 x 672`.
+- Standard metadata: timestamps and sensor/capture metadata.
+- Calibrated SPL intrinsics where geometric back-projection is required.
+
+Additional metadata includes ambient illumination measurements for sensing-condition analysis and per-frame pile-up metadata for studying transient distortion.
+
+## Task-wise Data Composition
+
+### Task 1: Depth Estimation
+
+![Depth estimation qualitative results](fig/Depth%20Estimation.png)
+
+This track evaluates depth recovery directly from raw photon time-of-flight histograms.
+
+| Component | Included | Description |
+| --- | --- | --- |
+| Raw SPAD histograms | Yes | `256 x 192 x 672` full time-of-flight waveform per sample |
+| SPL intrinsics | Yes | Used to back-project predicted depth maps into 3D |
+| Livox reference data | Yes | Auxiliary LiDAR point clouds/depth references for evaluation |
+| SPL-Livox extrinsics | Yes | Used to align SPL predictions with the Livox reference frame |
+| Camera poses | No | Not required for the single-view depth track |
+| Semantic labels | No | Not used in this track |
+| Light intensity metadata | No | Not used as input or supervision |
+
+Evaluation follows point-cloud geometry metrics after back-projecting predicted depth maps:
+
+- Chamfer Distance (CD, meters).
+- Recall under temporal-bin tolerances of `1`, `3`, and `5` bins.
+
+### Task 2: Multi-view 3D Reconstruction
+
+![Multi-view reconstruction qualitative results](fig/Multi-view%20reconstruction.png)
+
+This track evaluates reconstruction of scene geometry and novel-view rendering from multiple calibrated SPL views.
+
+| Component | Included | Description |
+| --- | --- | --- |
+| Raw SPAD histograms | Yes | Full transient waveform for each view |
+| SPL intrinsics | Yes | Used for geometric projection and view alignment |
+| Livox data | Yes | Supports pose estimation and geometric reference generation |
+| SPL-Livox extrinsics | Yes | Re-calibrated for reconstruction sequences |
+| Camera poses | Yes | Livox-SLAM poses transformed into the SPL frame |
+| Semantic labels | No | Not used in this track |
+| Light intensity metadata | No | Not used as input or supervision |
+
+The reconstruction subset contains `10` scenes, each with `20-40` transient views. For each scene, benchmark settings use `3`, `5`, or `10` input views for training and reserve the remaining views for novel-view rendering and geometry evaluation.
+
+Reported metrics cover three output domains:
+
+- Intensity rendering: SSIM and LPIPS.
+- Depth rendering: per-pixel `L1` error over valid pixels.
+- Histogram rendering: PSNR for methods that explicitly render transient histograms.
+
+### Task 3: 3D Semantic Segmentation
+
+![Semantic segmentation qualitative results](fig/Semantic%20Segmentation.png)
+
+This track evaluates semantic understanding from SPAD time-resolved measurements. Histograms are preprocessed, converted into single-photon point clouds through histogram-to-range projection, and then segmented with point-cloud backbones.
+
+| Component | Included | Description |
+| --- | --- | --- |
+| Raw SPAD histograms | Yes | Full transient waveform for each semantic frame |
+| SPL intrinsics | Yes | Device intrinsics for converting labeled bins to 3D points |
+| Livox data | No | Not used in this track |
+| SPL-Livox extrinsics | No | Not used in this track |
+| Camera poses | No | Not used in this track |
+| Semantic labels | Yes | Histogram-domain semantic annotations in `.npy` format |
+| Light intensity metadata | Yes | Ambient illumination recorded for each capture condition |
+
+The semantic subset contains `10,297` frames captured across `27` sequences and is split into:
+
+| Split | Samples |
+| --- | ---: |
+| Train | `8,297` |
+| Test | `2,000` |
+
+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.
+
+## Semantic Annotation Format
+
+![Annotation pipeline](fig/annotation_pipeline.png)
+
+STB uses histogram-domain semantic annotation to handle multi-return SPL measurements. Instead of assigning only one label to a pixel, the annotation is defined over temporal bins:
 
 ```text
-Annotations/
-  p1/
-    Sequence1.zip ... Sequence20.zip
-  p2/
-    Sequence21.zip ... Sequence27.zip
-
-Histgram/
-  p1.zip
-  p2.zip
-  depth_maps/
-    P1_Sequence1_depth.png ... P2_Sequence27_depth.png
-
-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
-
-DepthEstimate/
-  gt/
-    1.csv ... 10.csv
-  Histgram/
-    1.txt ... 10.txt
+S in {0, ..., C}^{N x B}
+N = H x W
+B = number of temporal bins
 ```
 
-## Data Summary
+The annotation pipeline identifies dominant peaks, assigns semantic labels to peak-support intervals, peels the labeled signal, and repeats the process to reveal weaker returns. This allows one pixel ray to contain multiple semantic entities at different ranges.
+
+## Statistics
+
+![Semantic category distribution](fig/semantic_category_distribution.png)
+
+![Light intensity distribution](fig/Light%20Intensity%20Distribution.png)
 
-- `Annotations/`: 27 zip files containing semantic annotation arrays.
-- Annotation arrays are stored as `.npy` files with dtype `uint8`.
-- A sampled annotation shape is `(49152, 672)`.
-- `Histgram/`: histogram data packaged as `p1.zip` and `p2.zip`, plus sequence-level depth maps.
-- `Reconstruction/`: reconstructed scene data packaged by location.
-- `DepthEstimate/`: depth-estimation ground truth CSV files and corresponding histogram TXT files.
+STB records sensing-condition statistics including range distribution, Signal-to-Background Ratio (SBR), Mean Photons Per Pixel (MPPP), ambient illumination, and pile-up indicators. These metadata are intended for dataset analysis and robustness studies rather than model input.
 
-## Notes
+## Expected Release Contents
 
-The annotation zip files contain one folder per sequence. The histogram files and annotation files are organized with matching sequence identifiers. Large files are kept compressed to make download and storage more practical.
+The released package is expected to include:
+
+- Raw SPAD histogram files.
+- Semantic annotations in `.npy` format.
+- Calibrated SPL intrinsics and distortion coefficients.
+- SPL-Livox extrinsics for tracks that require auxiliary LiDAR alignment.
+- Camera poses for multi-view reconstruction.
+- Timestamps and capture metadata.
+- Ambient illumination metadata.
+- Per-frame pile-up metadata.
+- Benchmark evaluation code, data-loading scripts, and annotation tools.
 
 ## License
 
-This dataset is released under the MIT license unless otherwise stated.
+The dataset is released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International license (CC BY-NC-SA 4.0).
+
+## Citation
+
+If you use SP-TransientBench, please cite the paper:
+
+```bibtex
+@inproceedings{sptransientbench2026,
+  title = {SP-TransientBench: A Real-Captured Single Photon Perception Benchmark},
+  author = {Sun, Shuoyao and others},
+  booktitle = {ECCV},
+  year = {2026}
+}
+```