eval3r 0.1.3

Handy toolkit for saving, evaluating, and visualizing 3D reconstruction predictions.

Eval3r: 3D reconstruction evaluation, made explicit

Overview

eval3r focuses on a small, explicit core:

• A stable on-disk prediction format (manifest + geometry + trajectory + cameras).
• A PredictionWriter / PredictionReader API for research code.
• Reliable geometry metrics — Chamfer (4 explicit variants), accuracy, completeness, F-score.
• Depth metrics — AbsRel, SqRel, RMSE, RMSE log, and delta accuracy (δ < 1.25).
• An e3r CLI for metric, validate, inspect, render, and preset.
• Optional headless rendering via pyrender.

The goals are reproducibility and explicit assumptions: no silent alignment, no silent unit conversion, no hidden default for pose conventions.

Install

Base install:

pip install eval3r

Optional extras:

pip install eval3r[render]   # pyrender + pillow + imageio
pip install eval3r[dev]      # pytest + ruff + mypy + pre-commit

Quick start — saving a prediction

import numpy as np
import eval3r as e3r

with e3r.PredictionWriter(
    "outputs/scannet/scene0799_00",
    scene_id="scene0799_00",
    dataset="scannet",
    method="my_method",
    unit="m",
    coordinate_system="opengl",
    pose_convention="T_wc",
) as pred:
    pred.save_point_cloud(points, colors=colors)
    pred.save_mesh(vertices, faces)
    pred.save_poses(poses, timestamps=timestamps)
    pred.save_metadata({"checkpoint": "ckpt.pth"})

The writer warns if unit, coordinate_system, or pose_convention is left unspecified — the manifest will record "unspecified" so downstream evaluation can flag the ambiguity instead of guessing.

Quick start — CLI

e3r validate outputs/scannet/scene0799_00
e3r inspect  outputs/scannet/scene0799_00
e3r metric all outputs/scannet/scene0799_00 \
    --gt /data/scannet/scene0799_00/gt_mesh.ply \
    --align none --samples 200000 --seed 42 \
    --thresholds 0.05 --chamfer-variant l1_mean_bidirectional
e3r render mesh outputs/.../geometry/pred_mesh.ply --out render.png --headless
e3r metric depth pred_depth.png --gt /data/scannet/scene0799_00/depth/0.png

Supported datasets

eval3r ships dataset adapters that describe standard filesystem layouts. Adapters own per-dataset assets (mesh, point cloud, depth, color, poses, intrinsics) and are discoverable through the registry:

dataset	GT format	description
scannet	mesh	ScanNet v2 — per-scene PLY meshes, RGB-D frames, camera poses
replica	mesh	Replica — high-quality indoor scene reconstructions
dtu	point cloud	DTU MVS — structured-light point clouds, evaluation subset of 19 scans
tanks_temples	point cloud	Tanks & Temples — laser-scan point clouds, training / intermediate / advanced subsets
eth3d	mesh / point cloud	ETH3D — high-res (dslr) and low-res (rig) tracks, COLMAP calibration
tum_rgbd	—	TUM RGB-D — handheld SLAM sequences (depth, color, poses, no geometry GT)

Benchmarking a method against a dataset

# List all registered adapters and inspect a specific one.
e3r datasets list
e3r datasets show scannet
e3r datasets show dtu

# Check that a dataset root matches the expected layout.
e3r datasets validate scannet --root /data/scannet \
    --split /data/scannet/splits/scannetv2_test.txt
e3r datasets validate dtu --root /data/dtu

# Run a method's predictions across a full split (generic command).
e3r benchmark run scannet outputs/scannet \
    --root /data/scannet \
    --split /data/scannet/splits/scannetv2_test.txt \
    --thresholds 0.05 --workers 8 \
    --out results.json --csv results.csv

# Run against datasets with non-default layout via adapter opts.
e3r benchmark run eth3d outputs/eth3d \
    --root /data/eth3d --track dslr

e3r benchmark run tanks_temples outputs/tnt \
    --root /data/tnt --subset training

# Pass adapter-specific overrides with -o key=value.
e3r benchmark run tum_rgbd outputs/tum \
    --root /data/tum -o intrinsics_fx=535.4 -o intrinsics_cx=320.1

eval3r does not ship dataset splits — pass a path to a text file with one scene id per line. Omit --split to auto-discover scenes. DTU defaults to the standard 19-scan evaluation subset; Tanks & Temples accepts --subset (training, intermediate, or advanced); ETH3D accepts --track (dslr or rig).

The benchmark reports two summaries: summary (mean / median / std over successful scenes only) and summary_all (over all scenes with missing or failed scenes filled in by the configured defaults — distance metrics → --missing-distance-default, default 1.0 m; F-score / precision / recall → --missing-fscore-default, default 0.0). The defaults are recorded in the result config so leaderboard numbers are reproducible.

The locator looks for eval3r_prediction.json first, then falls back to <scene>/mesh.ply, <scene>/<scene>_mesh.ply, <scene>/<scene>.ply, etc. Custom layouts are supported via --geometry-pattern "<scene>/out/final.ply" (repeatable, prepended to the defaults). Adapter overrides use -o key=value (e.g. -o depth_scale=5000, -o mesh_filename=scan.ply).

Chamfer variants

eval3r.metrics.chamfer_distance accepts an explicit variant:

variant	formula
l1_mean_bidirectional	0.5 * (mean‖p−q‖ + mean‖q−p‖)
l1_sum_bidirectional	mean‖p−q‖ + mean‖q−p‖
l2_squared	mean‖p−q‖² + mean‖q−p‖²
l2_unsquared	mean‖p−q‖ + mean‖q−p‖

Be explicit about which one a paper or another codebase reports.

Depth metrics

e3r metric depth pred_depth.png --gt gt_depth.png
e3r metric depth pred_depth.npy --gt gt_depth.npy --mask valid.npy
e3r metric depth pred_depth.png --gt gt_depth.png --json

metric	description
abs_rel	mean(|pred − gt| / gt)
sq_rel	mean((pred − gt)² / gt)
rmse	sqrt(mean((pred − gt)²))
rmse_log	sqrt(mean((log pred − log gt)²))
delta1	fraction of pixels where max(ratio, 1/ratio) < 1.25
delta2	same for threshold 1.25²
delta3	same for threshold 1.25³

Pixels where either depth is zero, negative, NaN, or Inf are excluded automatically. An optional --mask can further restrict valid pixels.

Alignment

Many reconstruction methods only predict geometry up to an unknown scale, rotation, and translation. To properly evaluate these methods, eval3r requires an explicit --align argument to align the prediction to the ground truth before computing metrics. The options are:

--align none      # default — never silently align
--align scale     # isotropic scale only
--align se3       # Umeyama R, t (or ICP without correspondences)
--align sim3      # Umeyama scale, R, t
--align icp       # point-to-point ICP from identity

Feedback

eval3r is a research-oriented project. Bug reports, feature requests, and general feedback are welcome — please open an issue on GitHub.

License

eval3r is released under the MIT License. See pyproject.toml for the canonical metadata.

eval3r does not redistribute any third-party datasets, splits, or ground-truth meshes. Datasets (ScanNet, Replica, DTU, ETH3D, Tanks & Temples, TUM RGB-D) remain under their original licenses; you must obtain them from their respective sources and abide by those terms. Adapter code in eval3r/datasets/ only describes filesystem layouts — no dataset content ships in the package.