depth-anything.cpp is verified numerically equal to the original Depth Anything 3
PyTorch model, component by component, not just on the final output. Every stage is
gated against tensors dumped from the reference model, and the end-to-end output is
checked against the genuine net() forward on real (non-fixture) images.
The gates live in the ctest suite (-DDA_BUILD_TESTS=ON, run ctest --test-dir build)
and the scripts/e2e_*.py end-to-end checks (which run the real PyTorch model and
compare). corr is the Pearson correlation of the flattened output vs the f32
reference; max|d| is the max absolute difference.
| path | result | notes |
|---|---|---|
| Depth | max|d|=9.5e-7, corr=1.000000 | f32 rounding noise; gate corr>0.999, max|d|<5e-3 |
| Camera pose | extrinsics max|d|=4.5e-8, intrinsics max|d|=2.6e-4 | focal magnitudes ~244-347 px |
| Multi-view depth+pose | corr=1.000000 | two structured views, cross-view attention |
| 3D Gaussian reconstruction (GIANT) | gated vs reference | GaussianAdapter -> world-space Gaussians + .ply |
| Nested metric depth | gated vs reference | two-branch alignment (anyview + metric) |
Ray-based pose (use_ray_pose) |
rotation max|d|=2.4e-7, intrinsics rel 2.3e-6 | aux ray head bit-exact; solver fed identical RANSAC indices |
| Native-resolution e2e | max|d|=1.37e-6, corr=1.000000 | raw arbitrary-resolution photo, bit-exact cv2 resize + the real net.head |
The resize is bit-exact vs OpenCV (computeResizeAreaTab / INTER_AREA + INTER_CUBIC
ported exactly), and the f32 forward is parity-preserving, so end-to-end depth lands at
f32-noise level (corr=1.0).
Quantized GGUFs preserve depth and pose relative to the f32 reference
(tests/test_quantize_accuracy.cpp):
| model | size | depth max|d| vs f32 | depth corr | ext max|d| |
|---|---|---|---|---|
| f32 | 393 MB | 0 (exact) | 1.000000 | 0 (exact) |
| q8_0 | 142 MB | 1.9e-3 | 0.999979 | 2.0e-3 |
| q4_k | 99 MB | 1.9e-2 | 0.998579 | 1.8e-2 |
q8_0 is near-lossless; q4_k stays above 0.998 correlation (well above the 0.99 floor) at the smallest size.
cmake -B build -DDA_BUILD_TESTS=ON && cmake --build build -j
ctest --test-dir build # 37 component + e2e gates
# end-to-end vs the genuine PyTorch model (needs the conversion venv)
python scripts/e2e_verify_native.py --model-dir models/DA3-BASE --gguf models/depth-anything-base-f32.gguf