euler-eval 2.11.0

Depth map evaluation toolkit with comprehensive metrics

euler-eval

A comprehensive evaluation toolkit for comparing predicted depth maps, RGB images, and camera ray direction maps against ground truth, powered by euler_loading for flexible dataset loading.

Features

• Depth metrics: PSNR, SSIM, LPIPS, FID, KID, AbsRel, RMSE, Scale-Invariant Log Error, Normal Consistency, Depth Edge F1
• RGB metrics: PSNR, SSIM, LPIPS, FID, SCE (Structural Chromatic Error), Edge F1, Tail Errors (p95/p99), High-Frequency Energy Ratio, Depth-Binned Photometric Error
• Rays metrics: ρ_A (AUC of angular accuracy curve), Angular Error statistics and threshold percentages
• Benchmark binning: Optional depth-range benchmark that subdivides metrics into square-root-scaled near/mid/far bins
• Sanity checking: Automatic validation of metric results against configurable thresholds, with detailed warning reports
• Sky masking: Optional exclusion of sky regions from metrics using GT segmentation
• Flexible dataset loading: Automatic loader resolution via euler_loading and ds-crawler index metadata
• Per-file and aggregate results: Outputs both per-image metrics and dataset-level aggregates to JSON, saved per-modality
• euler_train integration: Optional experiment logging via euler_train

Installation

Requires Python 3.9+.

uv pip install "euler-eval @ git+https://github.com/d-rothen/euler-parser.git"

# with euler_train logging support
uv pip install "euler-eval[logging] @ git+https://github.com/d-rothen/euler-parser.git"

# with clean-fid RGB FID backend support
uv pip install "euler-eval[fid] @ git+https://github.com/d-rothen/euler-parser.git"

Or install in editable mode:

pip install -e .

Dependencies

Core:

• numpy, scipy, Pillow
• torch, torchvision
• lpips
• tqdm
• euler-loading, ds-crawler

Optional:

• euler-train (install via [logging] extra)

Usage

The package provides a depth-eval console script:

depth-eval <config> [options]

It also provides a cache warmup helper for offline environments:

euler-eval.init

Or run directly:

python main.py <config> [options]

Before running on offline compute nodes, you can warm caches on a machine with network access:

HF_HOME=/shared/cache/hf \
TORCH_HOME=/shared/cache/hf/torch \
CLEANFID_CACHE_DIR=/shared/cache/clean-fid \
euler-eval.init

This pre-downloads:

• torchvision AlexNet weights
• torchvision Inception v3 weights
• LPIPS AlexNet weights
• the clean-fid inception checkpoint, if clean-fid is installed

Positional arguments

Argument	Description
config	Path to a JSON configuration file (see Configuration)

Options

Flag	Type	Default	Description
--device	{auto,cuda,cpu}	auto	Compute device (auto prefers CUDA when available)
--batch-size	int	16	Batch size for metrics that support batching
--num-workers	int	4	Number of data loading workers
--verbose, -v	flag	off	Enable verbose output
--skip-depth	flag	off	Skip depth evaluation
--skip-rgb	flag	off	Skip RGB evaluation
--skip-rays	flag	off	Skip rays (spherical direction map) evaluation
--mask-sky	flag	off	Mask sky regions from metrics using GT segmentation
--no-sanity-check	flag	off	Disable sanity checking of metric configurations
--metrics-config	str	auto-detect	Path to metrics_config.json for sanity checking
--depth-alignment	{none,auto_affine,affine}	auto_affine	Depth calibration mode; outputs are emitted in semantic native/metric spaces and depth aliases the canonical branch
--rgb-fid-backend	{builtin,clean-fid}	builtin	RGB FID backend; clean-fid requires optional dependency
--benchmark-depth-range	float float	none	Depth range [MIN, MAX] in meters for benchmark evaluation; computes depth and RGB metrics for pixels within this range, subdivided into square-root-scaled near/mid/far bins (additive to regular metrics)

Examples

# Evaluate with default settings (auto-selects CUDA when available)
depth-eval config.json --batch-size 32

# Evaluate with sky masking enabled (requires gt.segmentation in config)
depth-eval config.json --mask-sky -v

# Skip RGB evaluation, only evaluate depth
depth-eval config.json --skip-rgb

# Disable sanity checking
depth-eval config.json --no-sanity-check

# Disable depth alignment
depth-eval config.json --depth-alignment none

# Force affine scale+shift alignment on all depth predictions
depth-eval config.json --depth-alignment affine

# Use clean-fid for RGB FID computation
depth-eval config.json --rgb-fid-backend clean-fid

# Benchmark depth and RGB metrics within a depth range (near/mid/far bins)
depth-eval config.json --benchmark-depth-range 0.01 80.0

# Evaluate dense depth predictions against sparse pointcloud GT
depth-eval example_sparse_depth_config.json --skip-rgb --skip-rays

# Skip rays evaluation
depth-eval config.json --skip-rays

Benchmark Depth Bins

--benchmark-depth-range MIN MAX first filters valid GT depth pixels to MIN <= depth <= MAX. The near, mid, and far bins are then computed as three equal-width intervals in square-root depth space:

sqrt_min = sqrt(MIN)
sqrt_max = sqrt(MAX)
step = (sqrt_max - sqrt_min) / 3
near_max = (sqrt_min + step)^2
mid_max = (sqrt_min + 2 * step)^2

The interval bounds are near=[MIN, near_max), mid=[near_max, mid_max), and far=[mid_max, MAX]. For --benchmark-depth-range 0.01 80.0, this gives:

Bin	Depth interval
all	[0.01, 80.0]
near	[0.01, 9.290856529)
mid	[9.290856529, 35.954189863)
far	[35.954189863, 80.0]

Configuration

config.json

Defines GT modalities, prediction datasets to evaluate, and optional euler_train logging. See example_config.json. For sparse pointcloud depth GT, see example_sparse_depth_config.json.

{
  "euler_train": {
    "dir": "runs/my_project"
  },
  "gt": {
    "rgb":          { "path": "/data/gt/rgb" },
    "depth":        { "path": "/data/gt/depth" },
    "rays":         { "path": "/data/gt/rays" },
    "segmentation": { "path": "/data/gt/segmentation" },
    "calibration":  { "path": "/data/gt/calibration" }
  },
  "datasets": [
    {
      "name": "model_a",
      "rgb":   { "path": "/data/model_a/rgb" },
      "depth": { "path": "/data/model_a/depth" },
      "rays":  { "path": "/data/model_a/rays" },
      "output_file": "/path/to/output/model_a_eval.json"
    },
    {
      "name": "model_b_depth_only",
      "depth": { "path": "/data/model_b/depth" }
    },
    {
      "name": "model_c_rgb_only",
      "rgb": { "path": "/data/model_c/rgb" }
    }
  ]
}

Each modality entry can optionally include a split field to select a specific split from the dataset (e.g. { "path": "/data/gt/depth", "split": "test" }). euler-loading inline selectors are also accepted in path, such as /data/muses.zip:test or /data/muses.zip:test#scope=rgb.

For sparse pointcloud depth evaluation, use gt.sparse_depth instead of gt.depth. The prediction still uses datasets[].depth because the model output is a dense depth map. The evaluator projects the sparse GT point cloud into the prediction image plane using gt.intrinsics and gt.camera_extrinsics, then computes pointwise depth metrics only at projected valid pixels.

GT section

Field	Required	Description
gt.rgb.path	no*	Path to GT RGB dataset
gt.depth.path	no*	Path to GT depth dataset
gt.sparse_depth.path	no*	Path to sparse pointcloud GT dataset, e.g. sparse_depth with (N,C) points whose first columns are x,y,z in meters
gt.rays.path	no*	Path to GT ray direction map dataset (for rays evaluation)
gt.segmentation.path	no	Path to GT segmentation (needed for --mask-sky)
gt.calibration.path	no	Path to calibration data (camera intrinsics matrices)
gt.intrinsics.path	required with gt.sparse_depth	Path to camera intrinsics matrices for pointcloud projection
gt.camera_extrinsics.path	required with gt.sparse_depth	Path to source-to-camera extrinsics, e.g. MUSES lidar2rgb, for pointcloud projection
gt.name	no	Display name for ground truth (default: "GT")

* At least one of gt.rgb.path, gt.depth.path, gt.sparse_depth.path, or gt.rays.path is required.

Prediction datasets

Each entry in datasets can include rgb, depth, rays, or any combination:

Field	Required	Description
name	yes	Display name for this prediction dataset
rgb.path	no*	Path to predicted RGB dataset
depth.path	no*	Path to predicted dense depth dataset; also used when evaluating against sparse pointcloud GT
rays.path	no*	Path to predicted ray direction map dataset
output_file	no	Custom output path for results JSON (default: eval.json inside the first available modality path)

* At least one of rgb.path, depth.path, or rays.path is required.

euler_train section (optional)

When present, evaluation results are logged to an euler_train run. Requires the euler-train package to be installed (pip install euler-eval[logging]).

Field	Required	Description
euler_train.dir	yes	Project directory (creates a new run) or full path to an existing run directory (resumes it)

euler_train auto-detects whether the path is a run directory by checking for meta.json. When resuming an existing run, the run is detached after evaluation (the run remains active for further use). When a new run is created, it is finished upon completion.

Loader resolution

Loaders are resolved automatically by euler_loading from each dataset directory's ds-crawler index metadata. The index's euler_loading.loader and euler_loading.function fields determine which loader module and function to use (e.g. "vkitti2" maps to euler_loading.loaders.gpu.vkitti2).

No manual loader selection is required. Each dataset directory declares its own loader through its ds-crawler configuration.

Dataset metadata (e.g. radial_depth, rgb_range, sparse point columns, and coordinate units) is read automatically from each dataset's ds-crawler metadata via get_modality_metadata(). Depth and point-cloud coordinates are assumed to already be in meters.

Dataset Metadata

Each dataset directory or archive must contain ds-crawler metadata artifacts, typically generated under .ds_crawler/:

dataset-root/
  .ds_crawler/
    dataset-head.json
    ds-crawler.json
    index.json

When one physical root or archive contains several logical modalities, the artifacts may instead be scoped under .ds_crawler/<modality>/ with a .ds_crawler/scopes.json manifest. euler-eval passes modality scopes to euler-loading so paths such as /data/muses.zip:test can load .ds_crawler/rgb/index.json, .ds_crawler/depth/index.json, and related scoped artifacts from the same archive.

GT and prediction datasets are matched by hierarchy path and file ID through MultiModalDataset.

metrics_config.json

Controls sanity check thresholds. See metrics_config.json for all available options. When --metrics-config is not specified, the tool auto-detects metrics_config.json at the project root. If not found, built-in defaults are used.

Metrics

Depth metrics

Metric	Key	Description
PSNR	depth.image_quality.psnr	Peak Signal-to-Noise Ratio (dB), using max depth as dynamic range
SSIM	depth.image_quality.ssim	Structural Similarity Index
LPIPS	depth.image_quality.lpips	Learned Perceptual Image Patch Similarity
FID	depth.image_quality.fid	Fréchet Inception Distance (dataset-level distribution metric)
KID	depth.image_quality.kid_mean, kid_std	Kernel Inception Distance (mean and std)
Standard depth metrics	depth.standard.{image_mean,image_median,pixel_pool}.*	Monocular-depth metrics with explicit reducers: absrel, sqrel, mae, rmse, rmse_log, log10, silog, delta1, delta2, delta3
AbsRel	depth.depth_metrics.absrel	Absolute Relative Error (|pred-gt|/gt), reported as median and p90
RMSE	depth.depth_metrics.rmse	Root Mean Square Error, reported as median and p90
SILog	depth.depth_metrics.silog	Scale-Invariant Log Error, reported as mean, median, and p90
Normal Consistency	depth.geometric_metrics.normal_consistency	Surface normal angular error (degrees) via finite differences; includes mean, median, and percent below 11.25°/22.5°/30°
Depth Edge F1	depth.geometric_metrics.depth_edge_f1	Edge detection precision/recall/F1 for depth discontinuities

Sparse Depth Metrics

Sparse pointcloud GT evaluation reports only metrics that remain meaningful at isolated projected points. Dense image-quality and geometric metrics such as SSIM, LPIPS, FID, normals, and edge F1 are intentionally skipped. Serialized eval.json sparse-depth metrics use the namespace root sparsedepth (without an underscore), so flattened metric names match metricSet.metricNamespace = "sparsedepth.eval".

Metric	Key	Description
Standard depth metrics	sparsedepth.eval.{native,metric}.standard.{image_mean,image_median,pixel_pool}.*	Same monocular-depth reducers as dense depth, computed only at projected sparse GT pixels
AbsRel	sparsedepth.eval.{native,metric}.depth_metrics.absrel	Absolute Relative Error (|pred-gt|/gt), reported as median and p90 over projected sparse pixels
RMSE	sparsedepth.eval.{native,metric}.depth_metrics.rmse	Root Mean Square Error, reported as median and p90 over projected sparse pixels
SILog	sparsedepth.eval.{native,metric}.depth_metrics.silog	Scale-Invariant Log Error, reported as mean, median, and p90 over projected sparse pixels

RGB metrics

Metric	Key	Description
PSNR	rgb.image_quality.psnr	Peak Signal-to-Noise Ratio (dB)
SSIM	rgb.image_quality.ssim	Structural Similarity Index
SCE	rgb.image_quality.sce	Structural Chromatic Error
LPIPS	rgb.image_quality.lpips	Learned Perceptual Image Patch Similarity
FID	rgb.image_quality.fid	Fréchet Inception Distance (dataset-level distribution metric)
Edge F1	rgb.edge_f1	Edge preservation precision/recall/F1
Tail Errors	rgb.tail_errors	95th and 99th percentile per-pixel errors
High-Frequency Energy	rgb.high_frequency	HF energy preservation ratio (pred vs GT) and relative difference
Depth-Binned Photometric Error	rgb.depth_binned_photometric	MAE/MSE in near/mid/far depth bins (requires GT depth)

Rays metrics

Metric	Key	Description
ρ_A	rays.rho_a.mean, rho_a.median	Area Under the angular accuracy Curve — fraction of pixels with angular error ≤ threshold, integrated from 0 to a FoV-dependent threshold (S.FoV: 15°, L.FoV: 20°, Pano: 30°)
Angular Error	rays.angular_error.mean_angle, median_angle	Per-pixel angular error between predicted and GT camera ray directions (degrees)
Angular Error Thresholds	rays.angular_error.percent_below_*	Percentage of pixels with angular error below 5°, 10°, 15°, 20°, 30°

Output

Results are saved as JSON per modality per prediction dataset (one file for depth or sparse depth, one for RGB, one for rays). Default path: eval.json inside each modality's dataset path, unless overridden by output_file in the config.

For RGB FID, two backends are available:

• builtin: in-process Inception-based implementation in this repository.
• clean-fid: delegates folder-vs-folder FID computation to clean-fid. This backend requires installing the optional fid extra and is recommended when you need scores closer to standard published FID numbers.

When --rgb-fid-backend clean-fid is used, euler-eval will honor CLEANFID_CACHE_DIR if set:

• If CLEANFID_CACHE_DIR/inception-2015-12-05.pt exists, it is staged into the location clean-fid expects before evaluation.
• If it does not exist and the machine is online, euler-eval asks clean-fid to download it into CLEANFID_CACHE_DIR.
• Without CLEANFID_CACHE_DIR, clean-fid falls back to its own default local path handling.

Output structure

{
  "depth_native": { "...": "native model depth space, if diagnostically meaningful" },
  "depth_metric": { "...": "metric depth space, if available" },
  "depth": {
    "...": "canonical alias of depth_metric when present, else depth_native"
  },
  "sparsedepth": {
    "eval": { "...": "serialized sparse-depth metrics when gt.sparse_depth is used" }
  },
  "rgb": {
    "...": "..."
  },
  "per_file_metrics": {
    "children": {
      "scene_01": {
        "children": {
          "camera_0": {
            "files": [
              {
                "id": "frame_0001",
                "metrics": {
                  "depth": { "...": "canonical alias" },
                  "depth_native": { "...": "native, when emitted" },
                  "depth_metric": { "...": "metric, when emitted" },
                  "sparsedepth": { "eval": { "...": "serialized sparse-depth metrics" } },
                  "rgb": { "...": "..." }
                }
              }
            ]
          }
        }
      }
    }
  }
}

For depth outputs:

• depth_native: the model's native depth space after spatial/radial preprocessing, emitted only when it is diagnostically distinct.
• depth_metric: the comparable metric-depth branch. This is either the native prediction itself or the calibrated scale-shift result.
• depth: canonical alias of depth_metric when available, otherwise depth_native.
• standard: explicit monocular-depth metrics with three reducers: image_mean, image_median, and pixel_pool.

For sparse depth outputs, the internal Python result dict still uses sparse_depth_native, sparse_depth_metric, and sparse_depth, but serialized eval.json metric paths are rooted at sparsedepth.eval to satisfy external namespace validation. Only standard and depth_metrics categories are present.

Previous single-depth structure (kept under depth) is:

{
  "depth": {
    "image_quality": {
      "psnr": 28.5,
      "ssim": 0.92,
      "lpips": 0.08,
      "fid": 12.3,
      "kid_mean": 0.005,
      "kid_std": 0.002
    },
    "standard": {
      "image_mean": {
        "absrel": 0.08,
        "sqrel": 0.04,
        "mae": 0.62,
        "rmse": 1.20,
        "rmse_log": 0.11,
        "log10": 0.04,
        "silog": 0.08,
        "delta1": 0.91,
        "delta2": 0.97,
        "delta3": 0.99
      },
      "image_median": {
        "absrel": 0.07,
        "sqrel": 0.03,
        "mae": 0.58,
        "rmse": 1.10,
        "rmse_log": 0.10,
        "log10": 0.04,
        "silog": 0.07,
        "delta1": 0.92,
        "delta2": 0.98,
        "delta3": 0.99
      },
      "pixel_pool": {
        "absrel": 0.08,
        "sqrel": 0.04,
        "mae": 0.61,
        "rmse": 1.18,
        "rmse_log": 0.11,
        "log10": 0.04,
        "silog": 0.08,
        "delta1": 0.91,
        "delta2": 0.97,
        "delta3": 0.99
      }
    },
    "depth_metrics": {
      "absrel": { "median": 0.05, "p90": 0.12 },
      "rmse":   { "median": 1.2, "p90": 3.1 },
      "silog":  { "mean": 0.08, "median": 0.06, "p90": 0.15 }
    },
    "geometric_metrics": {
      "normal_consistency": {
        "mean_angle": 12.3,
        "median_angle": 9.8,
        "percent_below_11_25": 55.2,
        "percent_below_22_5": 82.1,
        "percent_below_30": 91.5
      },
      "depth_edge_f1": {
        "precision": 0.72,
        "recall": 0.68,
        "f1": 0.70
      }
    },
    "dataset_info": {
      "num_pairs": 500,
      "gt_name": "GT",
      "pred_name": "model_a"
    }
  },
  "rgb": { "...": "unchanged" }
}

Sanity check report

When sanity checking is enabled (the default), a sanity_check_report.json is saved to the current working directory containing warnings grouped by metric type.

License

MIT