Cloud Point RPC

Communication JSON RPC protocol and implementation with Unity Scene.

Project Structure

• include/: Header files for the RPC server, TCP server, and C-API.
• src/: Implementation of the RPC logic, networking, and C-API.
• src/cloud_point/: OpenCV-based image processing and rectification logic.
• docs/: Documentation diagrams and models.
• subprojects/: Dependencies managed by Meson.

Status

• Server implementation with C-API for Unity
• OpenCV stereo client (StereoRectifier, PointCloudBuilder, CloudPointClient facade)
• Unity-side C# implementation per docs/unity-integration.md

API Documentation

See API.md for detailed request/response formats.

Pipeline

Unity acts as a data source: it serves stereo image pairs (get-image-pair) and full stereo calibration (get-stereo-calibration) over JSON-RPC 2.0. The C++ CloudPointClient calls connect() once to fetch calibration, then on each compute_cloud() call it fetches a synchronised image pair, runs stereo rectification (StereoRectifier, cv::stereoRectify + remap), computes disparity with SGBM (16× scaling), reprojects to 3-D with cv::reprojectImageTo3D (PointCloudBuilder), filters NaN/invalid points, and returns a PointCloud. An optional write_ply() helper serialises the result to disk.

See API.md for wire schemas and docs/unity-integration.md for the Unity C# design spec.

Development

The project uses Meson build system and C++23.

Dependencies

• Meson (>= 1.1.0), Ninja
• GCC/Clang (C++23 support)
• Git (for subprojects)
• OpenCV 4 (optional; required for stereo point cloud compute)

The following dependencies are managed via Meson subprojects:

• ASIO (Networking)
• nlohmann/json (JSON serialization)
• yaml-cpp (Configuration loading)
• glog (Logging)
• jsonrpccxx (JSON-RPC 2.0 implementation)

Build & Run

meson setup build
meson compile -C build
./build/src/cloud_point_rpc_server config.yaml

Note: You need a config.yaml file. See config.yaml.example for the required format.

Run the interactive CLI client:

./build/src/cloud_point_rpc_cli config.yaml

CLI menu options (OpenCV options are hidden when built without opencv4):

Option	Action
1	List available RPC methods
2	Get intrinsic params (legacy)
3	Get extrinsic params (legacy)
4	Compute point cloud — prints point count and bounding box
5	Compute point cloud and save to output.ply
0	Exit

Build on windows

It's assumed that you have GCC and make/ninja installed on your system (and available in PATH)

## FIRST OF ALL!
git submodule init
git submodule update
# Next python:
python3 -m venv .\venv
.\venv\Scripts\Activate.ps1
# or
.\venv\bin\Activate.ps1
pip install meson cmake
meson setup -Ddefault_library=static build
meson compile -C build
# To correctly get dlls:
meson devenv -C build
## .\build\tests\unit_tests < for dummy test
## .\build\src\.. < produced execs and libs

Testing

meson test -C build -v

Docker

You can build and run the cli using Docker.

1. Build Image

docker build -t cloud-point-rpc .

2. Run Container

The cli will try to connect to a running server on ip and port defined in config.yml file. (defined in config.yaml inside the image). For simplicity, it's better to use a host network, so you will not have any headache with accessability.

Server is not configured to run through container, if you need, contact me

You also can mount your own config.yaml to override the default settings:

docker run --network=host -it -v $(pwd)/my_config.yaml:/app/config.yaml cloud-point-rpc

Validation with SCARED Dataset

The scared_dataset_server executable lets you validate the stereo point-cloud pipeline against real endoscopic images from the SCARED dataset.

Obtaining the data

1. Download test_dataset_8.zip from https://huggingface.co/datasets/maxhallan7/scared.
2. Extract so that keyframe_0/ through keyframe_4/ exist under test_dataset_8/.

Each keyframe directory contains:

• Left_Image.png, Right_Image.png — 1280×1024 unrectified RGBA images.
• endoscope_calibration.yaml — OpenCV FileStorage with M1, D1, M2, D2, R, T nodes.

Note: T is stored in millimetres in the YAML file (baseline ≈ −4.35 mm). scared_dataset_server divides T by 1000 before placing it on the wire (the wire protocol uses metres).

Running the server

./build/src/cloud_point/scared_dataset_server \
    /path/to/test_dataset_8/keyframe_0 8080

If port 8080 is already taken on your machine (Docker's rootlesskit commonly holds it) pass another port and update server.port in the CLI config accordingly. Connecting the CLI to a foreign service on 8080 shows up as invalid JSON response from server / std::bad_alloc errors.

Connecting with the CLI

In a second terminal run the interactive CLI with the SCARED-tuned config:

./build/src/cloud_point_rpc_cli config.scared.yml
# Option 4 — compute point cloud and print valid point count + bounding box
# Option 5 — compute point cloud and save a triangulated PLY mesh

config.scared.yml sets the optional cloud_point section that options 4/5 honour:

cloud_point:
  algorithm: cpu        # "gpu" falls back to CPU when CUDA is unavailable
  num_disparities: 160  # fx ~1024 px, baseline ~4.35 mm -> up to ~160 px
  min_depth_m: 0.02     # endoscopic working range: 20 mm .. 300 mm
  max_depth_m: 0.30
  ply_stride: 1         # option 5: 4 = 16x smaller, block-averaged mesh
  wls_filter: false     # true = smoother mesh for viewers, less accurate

Depth limits are a physical bound on the scene: with this rig depth is roughly 4.45 m / disparity_px, so any mismatch with a disparity below ~15 px reprojects metres away. Without the section the CLI falls back to the generic defaults (GPU, 128 disparities, 0.01–10 m). The SGBM matcher itself is configured with OpenCV's recommended smoothness penalties (P1 = 8·bs², P2 = 32·bs²), a 5×5 block, uniqueness ratio 10, speckle filtering and a left-right consistency check; see CpuStereoMatcher::Params.

Checking the result

Option 4 should report a bounding box with z inside roughly [0.03, 0.16] m for test_dataset_8 keyframes. Option 5 writes a binary little-endian PLY containing every valid point and a mesh triangulated from the pixel grid (triangles are dropped where the longest edge exceeds 5 % of the local depth, so the mesh breaks at occlusions). The mesh is what makes web viewers usable: viewers such as Meshy's online PLY viewer fabricate a triangle from every three consecutive vertices of a vertex-only PLY, which draws long slivers across the surface and makes a correct cloud look like a fan of rays. Pass PlyOptions{false, 0.0f, false} to write_ply for a points-only ASCII file.

Surface roughness vs accuracy. SGBM's sub-pixel disparity noise (~0.2 px, correlated over several pixels) is ~1 mm of depth on this rig, far more than the 0.07 mm lateral pixel pitch, so a mesh built from the raw cloud is "hairy": face normals sit a median 45° off the camera axis on tissue that faces the camera. Colour depth maps hide this; shaded mesh viewers show it as fuzz. wls_filter: true applies OpenCV's edge-aware WLS disparity filter (needs opencv_ximgproc, doubles matching time) and brings the median normal to ~23° with neighbour depth jumps down from 0.15 mm to 0.06 mm, but it also costs accuracy on SCARED (keyframe 1: MAE 0.84 → 0.92 mm, within 2 mm 80 → 78 %; dataset_3: MAE 1.75 → 2.20 mm). It is therefore off by default: use it for pictures, not for measurements. SGBM's own holes are never filled by the filter.

When the keyframe contains point_cloud.obj, run the benchmark to compare the reconstruction with its pixel-aligned XYZ ground truth:

./build/src/cloud_point/scared_dataset_benchmark \
    /path/to/dataset_1/keyframe_1 160 [depth.png|-] [min_depth_m max_depth_m]

The benchmark always reports the valid-point fraction, depth percentiles and matching/reconstruction timings as JSON, and optionally writes a colour-mapped depth image (third argument, - to skip) for visual inspection. The optional depth range applies the same filter as the CLI's cloud_point section; without it the generic 0.01–10 m defaults are used, which lets a few residual mismatches at metres of depth inflate RMSE. When point_cloud.obj is present it also reports coverage, component-wise and 3-D errors and threshold accuracy. Note that the test_dataset_* archives ship without point_cloud.obj; ground truth is only in the full dataset_N.zip archives (13–40 GB each). The zips are served with HTTP range support, so single keyframes can be extracted remotely with Python's zipfile over a seekable HTTP file object instead of downloading the whole archive.

scripts/scared_overview.py runs the benchmark over many keyframes and prints a Markdown table:

scripts/scared_overview.py --png-dir out/depth --depth-range 0.02 0.30 \
    datasets/scared/dataset_1/keyframe_* datasets/scared/test_dataset_8/keyframe_*
``` OBJ coordinates are
converted from millimetres to metres and rectified into the same left-camera
frame as the reconstructed cloud before evaluation. Reference numbers for
`dataset_1/keyframe_1` with the tuned SGBM configuration: coverage ≈ 0.85,
MAE₃D ≈ 0.8 mm, RMSE₃D ≈ 1.4 mm, 80 % of points within 2 mm (the previous
unregularised SGBM gave MAE₃D ≈ 44 mm and RMSE₃D ≈ 289 mm).

The E2E test (`tests/test_scared_dataset.cpp`) exercises the same pipeline
with `num_disparities = 160` and asserts >50 000 valid points and a median
depth in `[0.02, 0.20]` m.

## Communication model

![Communicatoin model plantuml diagram](docs/cm.png)