SP-TransientBench: A Real-Captured Single Photon Perception Benchmark

Paper | Dataset download

Dong, H., Zhang, Z., Wen, Z., Qiang, Y., Deng, R., Dong, W., Jiang, Z., Li, X., Lu, R., Sun, S., Wang, W., Xia, Z., Zheng, H., Shi, G., & Ren, X. SP-TransientBench: A Real-Captured Single Photon Perception Benchmark. arXiv:2606.18952, 2026.

SP-TransientBench (STB) is a real-captured benchmark for single-photon LiDAR (SPL) perception in photon-starved 3D scenes. It provides full per-pixel time-of-flight histograms, calibrated metadata, task-specific supervision, and official splits for depth estimation, multi-view 3D reconstruction, and 3D semantic segmentation.

STB contains 10,297 views captured with a solid-state single-photon LiDAR at 256 x 192 spatial resolution. Each view stores a full transient waveform with 672 temporal bins, preserving photon sparsity, background noise, and multi-return structures that are often lost in depth-only releases.

Capture Setup

STB is captured with an Adaps ADS6311 Hawk solid-state SPL device operating under 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.

Benchmark Tracks

STB is organized around three complementary tasks:

Track	Scale	Purpose
Depth estimation	10 samples	Recover single-view depth directly from raw transient histograms
Multi-view 3D reconstruction	10 scenes, 20-40 views per scene	Reconstruct geometry and render novel views from calibrated SPL captures
3D semantic segmentation	27 sequences, 10,297 frames	Segment SPAD-derived 3D observations with histogram-domain semantic labels

Every track includes raw SPAD histograms. Depending on the task, the release also includes SPL intrinsics, SPL-Livox extrinsics, reference Livox point clouds, camera poses, ambient illumination metadata, pile-up metadata, and semantic annotations.

Layout

SP-TransientBench/
|-- README.md
|-- fig/                    figures used in this dataset card
|-- DepthEstimate/          depth-estimation histograms and Livox references
|-- Reconstruction/         multi-view reconstruction scene archives
|-- Annotations/            semantic annotation archives
|-- Histgram/               semantic-track raw histograms and depth maps
|-- codes/                  annotation, point-cloud conversion, simulation, and reconstruction tools
`-- config/                 SPL calibration, extrinsics, parsing settings, and IRF data

The full release is approximately 168.7 GB. Large files are stored in the Hugging Face dataset repository and should be downloaded from the dataset files page or with Git LFS/Xet-compatible tooling.

git lfs install
git clone https://huggingface.co/datasets/shuinb/SP-TransientBench

Data

Depth estimation samples pair raw SPL histograms with Livox reference point clouds:

DepthEstimate/
|-- Histgram/
|   `-- 1.txt ... 10.txt
`-- gt/
    `-- 1.csv ... 10.csv

DepthEstimate/Histgram/{id}.txt stores one flattened 256 x 192 SPL histogram grid, with 672 photon-count bins per valid row. DepthEstimate/gt/{id}.csv stores the corresponding Livox reference point cloud with metric X,Y,Z coordinates and capture metadata.

Multi-view reconstruction scenes are released as compressed scene packages:

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

After decompression, each scene follows the same structure as AI_floor2:

AI_floor2/
|-- RawDataHistogramMap_frame_0_<timestamp>.txt
|-- 1.csv ... 26.csv
|-- sp_pose_results.csv
|-- sp_merged_map.ply
`-- json/
    |-- three_views/
    |   |-- train.json
    |   `-- test.json
    |-- five_views/
    |   |-- train.json
    |   `-- test.json
    `-- ten_views/
        |-- train.json
        `-- test.json

The RawDataHistogramMap_frame_0_<timestamp>.txt files store SPL views, {view_id}.csv files store matched Livox point clouds, sp_pose_results.csv records Livox and SPL poses as flattened 4 x 4 transforms, and sp_merged_map.ply provides the registered scene-level reference map. The json/ folders define official 3, 5, and 10 input-view reconstruction splits in a NeRF-style format.

Semantic segmentation data are split by SPL device:

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

Inside each sequence archive, semantic labels are stored as .npy arrays named like RawDataHistogramMap_frame_*_semantic.npy. Each array has shape (49152, 672), where 49152 = 256 x 192, and stores uint8 semantic ids with 0 for unlabeled/background bins and 1-13 for semantic classes.

Code

The codes/ folder contains utility code released with STB:

• codes/annotation tool/labelLidarwave.py: a single-photon LiDAR waveform annotation tool.
• codes/pointcloud_extract/raw2pc.py: converts raw histogram .txt files into point clouds using the maximum-bin histogram method.
• codes/pointcloud_extract/ann2pc.py: converts annotated .npy semantic data into corresponding semantic point clouds.
• codes/simulator/: simulation tools used for STB experiments.
• codes/reconstruction/: STB reconstruction code based on the TransientNeRF method.

Task Details

Depth Estimation

This track evaluates depth recovery directly from raw photon time-of-flight histograms. Predictions are back-projected to 3D with calibrated SPL intrinsics and compared against Livox references with Chamfer Distance (CD, meters) and Recall under 1, 3, and 5 temporal-bin tolerances.

Multi-view 3D Reconstruction

This track evaluates scene reconstruction and novel-view rendering from multiple calibrated SPL views. Each scene provides sparse-view settings with 3, 5, or 10 input views for training and reserves the remaining views for evaluation. Reported metrics cover intensity rendering (SSIM, LPIPS), depth rendering (L1 error), and histogram rendering (PSNR).

3D Semantic Segmentation

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 segmented with point-cloud backbones.

Split	Samples
Train	8,297
Test	2,000

The semantic track uses 13 foreground classes. Evaluation reports Overall Accuracy (OA) and mean Intersection-over-Union (mIoU), averaged over three random seeds in the paper protocol.

Semantic Labels

STB uses histogram-domain semantic annotation to handle multi-return SPL measurements. Instead of assigning a single label to each pixel, annotations are defined over temporal bins:

S in {0, ..., C}^{N x B}
N = H x W
B = number of temporal bins

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 lets a single pixel ray contain multiple semantic entities at different ranges.

Contents

File or folder	Purpose
DepthEstimate/	Single-view depth-estimation samples with raw histograms and Livox point-cloud references
Reconstruction/	Scene packages for sparse-view SPL reconstruction and novel-view evaluation
Annotations/	Histogram-domain semantic label packages for 27 sequences
Histgram/	Raw histogram packages and sequence-level depth maps for the semantic track
codes/	Annotation, point-cloud conversion, simulation, and TransientNeRF-based reconstruction code
config/config.yaml	SPL intrinsics, distortion coefficients, temporal bin width, image size, and parsing settings
config/IRF_global.csv	Global instrument response function (IRF) measurements for transient calibration and analysis
config/final_extrinsic_sp_to_livox.txt	SPL-to-Livox extrinsic calibration used for cross-sensor alignment
config/offset.txt	Additional extrinsic offset parameters used for SPL/Livox alignment
fig/	Dataset-card figures, qualitative examples, annotation diagrams, and statistics

Statistics

STB records sensing-condition metadata such as ambient illumination and pile-up indicators. These metadata are intended for dataset analysis and robustness studies rather than model input.

License

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:

@misc{dong2026sptransientbench,
  title         = {SP-TransientBench: A Real-Captured Single Photon Perception Benchmark},
  author        = {Dong, Hongzhou and Zhang, Zili and Wen, Ziting and Qiang, Yiheng and Deng, Runrong and Dong, Wenle and Jiang, Ziwen and Li, Xinyang and Lu, Rui and Sun, Shuoyao and Wang, Wenyu and Xia, Ziyi and Zheng, Haitao and Shi, Guodong and Ren, Xiaoqiang},
  year          = {2026},
  eprint        = {2606.18952},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CV},
  doi           = {10.48550/arXiv.2606.18952}
}