Update README.md

README.md

@@ -1,3 +1,575 @@
 ---
-license: apache-2.0
+license: other
+license_name: nvidia-open-model-license
+license_link: >-
+  https://developer.download.nvidia.com/licenses/NVIDIA-OneWay-Noncommercial-License-22Mar2022.pdf?t=eyJscyI6ImdzZW8iLCJsc2QiOiJ
 ---
+
+## Description:
+
+MetricTrail estimates camera motion and per-frame metric depth from long RGB
+image sequences for visual odometry and 3D reconstruction research. It combines
+single-image metric depth, learned sparse correspondences, and local geometric
+optimization, with supplied or RGB-estimated intrinsics and optional rectified
+stereo. MetricTrail 1.0 was developed by NVIDIA.
+
+The repository provides a CLI that exports camera trajectories and caches
+per-frame depth. Model weights are not included in the source repository;
+the DINO-DPVO checkpoint is planned for a separate Hugging Face release.
+The CLI does not export dense point clouds or meshes. The dense-reconstruction
+results below use a separate research evaluation workflow.
+
+This model is for research and development only.
+
+### License/Terms of Use:
+
+Use of the model weights is governed by the [NVIDIA License](https://developer.download.nvidia.com/licenses/NVIDIA-OneWay-Noncommercial-License-22Mar2022.pdf), which limits use to noncommercial research or evaluation, subject to the agreement. Use of the DPVO base model is governed by the [MIT License](https://opensource.org/license/mit). Original NVIDIA-authored source and documentation marked Apache-2.0 use the [Apache License 2.0](../LICENSE). Incorporated source, separately obtained upstream weights, and datasets retain their applicable terms; DINOv3 uses the [DINOv3 License](https://github.com/facebookresearch/dinov3/blob/main/LICENSE.md). See [THIRD_PARTY_NOTICES.md](../THIRD_PARTY_NOTICES.md) and the [repository license scope](../README.md#license) for inherited MIT and BSD terms and the removal of legacy retrieval, alignment, and optical-flow helpers. Separately obtained retrieval tools retain their own applicable terms. Source licenses do not replace model-weight or dataset terms.
+
+### Deployment Geography:
+
+Global
+
+### Use Case:
+
+- Research on long-video camera-motion estimation and metric 3D reconstruction.
+- Studying metric-depth priors, visual representations, and sparse local
+  optimization for visual odometry and mapping.
+- Reconstructing indoor and outdoor scenes for computer vision, graphics,
+  simulation, and robotics research, with independent validation of geometry.
+- Comparing calibrated monocular, RGB-only calibration, and calibrated stereo
+  reconstruction under the reported protocols.
+
+### Release Date:
+
+Hugging Face 10/23/2026 via [https://huggingface.co/nvidia/metrictrails](https://huggingface.co/nvidia/metrictrails)
+
+## References(s):
+
+- *MetricTrail: Revisiting the Power of Metric Depth and Local Optimization for
+  Long-Sequence Streaming 3D Reconstruction*.
+- [Repository overview and running instructions](../README.md).
+- [DPVO](https://github.com/princeton-vl/DPVO), the sparse local odometry backbone.
+- [DINOv3](https://github.com/facebookresearch/dinov3) and the
+  [ViT-S/16 LVD-1689M pretrained model](https://huggingface.co/facebook/dinov3-vits16-pretrain-lvd1689m).
+- [MoGe](https://github.com/microsoft/MoGe) and
+  [MoGe-2](https://arxiv.org/abs/2507.02546), the default metric-depth provider.
+- [SALAD](https://github.com/serizba/salad), an optional external place-retrieval
+  tool obtained and run independently of MetricTrail.
+- [ViPE](https://github.com/nv-tlabs/vipe), used for RGB-only shared-focal calibration.
+- [RoMa](https://github.com/Parskatt/RoMa), used for optional revisit-based focal refinement.
+
+## Model Architecture:
+
+**Architecture Type:** Learned recurrent visual odometry with frozen visual
+foundation-model features and explicit bundle adjustment.
+
+**Network Architecture:** DPVO's sparse patch correspondence network with
+learned residual feature-fusion adapters, a frozen DINOv3-S/16 encoder, and a
+frozen MoGe-2 metric-depth provider. The complete system includes camera
+calibration and optional pose-graph correction.
+
+**Number of model parameters:** The DINO-DPVO checkpoint for the separate
+Hugging Face release contains
+**3,425,860 learned parameters (3.43 million)** across 100 FP32 weight/bias
+tensors. This count excludes the frozen DINOv3 backbone, external MoGe-2 depth
+model, calibration dependencies, and optional SALAD and RoMa models.
+
+The local estimator alternates learned patch matching with joint optimization
+of camera poses and sparse patch inverse depths in a bounded active window.
+MoGe-2 independently predicts metric camera-Z depth for each image. These
+predictions initialize patch depths and remain robust soft constraints during
+optimization; multi-view evidence can override unreliable prior measurements.
+In the dense-reconstruction workflow, frame-level depth maps are
+placed in a shared world frame using the recovered poses and are not themselves
+bundle-adjustment variables.
+
+Frozen DINOv3 features augment the matching map and recurrent patch context
+through learned residual adapters. Its 384-dimensional features are projected
+to 64 channels and split into two 32-channel branches that augment the
+128-channel matching stream and 384-dimensional recurrent context. Final
+residual projections are initialized to zero so adaptation starts from the
+official DPVO checkpoint's predictions.
+
+We train MetricTrail for 15,000 optimizer steps with global batch size 16 on
+16 NVIDIA A100 80GB GPUs, taking approximately seven hours. Fusion branches
+first warm up on 5,000 TartanAir clips with odometry frozen; odometry and fusion
+branches are then jointly fine-tuned on the remaining 235,000 clips. DINOv3 stays
+frozen, and MoGe-2 is not used during adaptation. Joint training uses AdamW,
+learning rates of 8e-6 for odometry and 8e-5 for fusion, weight decay 1e-6,
+linear OneCycle annealing with 1% warmup, gradient clipping at 10, and seed 1234. Each clip
+uses 80 patches per frame and 18 recurrent refinements. The frozen DINOv3
+forward pass uses BF16; adapters, odometry, differentiable bundle adjustment,
+and losses use FP32.
+
+<!--
+## Computational Load (internal review only)
+
+Remove this entire comment from public source and the published model card.
+
+Scope: added compute for the documented MetricTrail adaptation run, including
+fusion warmup and joint fine-tuning. The run uses 16 A100 80GB GPUs for
+approximately 7 hours (112 GPU-hours), processing 240,000 clips of 15 frames at
+480 x 640 with 80 patches per frame and 18 recurrent refinements. Third-party
+DPVO and DINOv3 pretraining, evaluation, and discarded exploratory runs are
+excluded. MoGe-2 is not used during adaptation.
+
+**Cumulative Compute (estimated): 1.3 x 10^18 FLOPs.**
+This is an analytical operation estimate, not a hardware-profiler measurement.
+Counting a multiply-add as two FLOPs gives approximately 2.8 x 10^17 FLOPs for
+frozen DINOv3 forward passes, 2.5 x 10^17 for the DPVO convolutional encoders,
+and 6.9 x 10^17 for recurrent-update matrix operations. The encoder/update
+counts approximate forward plus backward as three times forward, with
+adjustments for the frozen warmup. The subtotal is 1.22 x 10^18 FLOPs; the
+reported value includes an approximate allowance for correlation, adapters,
+bundle adjustment, losses, and other operations. Random edge pruning and
+kernel details are not reconstructed. A reasonable sensitivity range is
+0.8 x 10^18 to 2.0 x 10^18 FLOPs, not a validated confidence interval.
+Sources for the operation count: DPVO/dpvo/net.py, DPVO/dpvo/extractor.py,
+DPVO/dpvo/blocks.py, and metrictrail/runtime/dino_fusion.py.
+
+**Estimated Energy for Model Training: 50 kWh.**
+An ORD hardware query on 10/20/2026 confirmed NVIDIA A100-SXM4-80GB with
+configured and default power limits of 400 W. Applying this rating to the
+documented training run, the prescribed calculation is
+16 x 0.400 kW x 0.80 x 7 hours x 1.4 PUE = 50.176 kWh.
+The 80% power fraction and PUE of 1.4 are the internal guidance defaults,
+not measured power or a verified ORD facility PUE.
+
+**Estimated Emissions for Model Training: 0.021 tCO2e (approximately 21 kgCO2e).**
+50.176 kWh x 410.5 gCO2e/kWh x 0.000001 = 0.020597 tCO2e.
+The emissions factor is the guidance's fallback value; a site-specific
+electricity emissions factor was not available. These are estimates for
+the documented adaptation run, not metered energy or emissions.
+
+Methods:
+https://nvidia.sharepoint.com/sites/TrustworthyAI/SitePages/How-to-Assess-Training-Compute.aspx
+https://nvidia.sharepoint.com/:w:/s/TrustworthyAI/Ecb23D6b-kNLv1c6LCdhSiMBjWFz0MKncBaRcryDsfv-vA?e=lIcO9z
+Hardware specifications: https://www.nvidia.com/en-us/data-center/a100/
+-->
+
+## Input:
+
+**Input Type(s):** A temporally ordered RGB image sequence and camera parameters;
+optionally synchronized left/right image sequences for stereo. A JSON
+configuration selects the data adapter and processing settings.
+
+**Input Format(s):** Prepared PNG frames or KITTI NPZ files containing `images`
+as `uint8 [N,3,H,W]`. The repository also supplies Oxford Spires and LaMAria
+inventory adapters. Loaders produce CHW RGB `float32` images in `[0,1]`.
+Arbitrary video containers are not decoded by this CLI. See the
+[input layouts](../README.md#layout-of-external-files) and
+[data adapters](../metrictrail/datasets.py).
+
+Optional loop correction consumes a saved local trajectory NPZ, its successful
+JSON sidecar, and an externally generated candidate JSON containing frame pairs
+and scores. The candidate file records the baseline NPZ's SHA256; the candidate
+file's own SHA256 must also be supplied.
+
+**Input Parameters:** A configured frame inventory with at least 16 images,
+positive FPS, and image dimensions `(H,W)`. Intrinsics use `[fx,fy,cx,cy]` in
+processed-image pixel coordinates. In calibrated KITTI mode, `intrinsics`, `K`,
+or `input_intrinsics` in the NPZ may be a four-vector, a 3 x 3 matrix, or an
+`[N,4]` array; the last format uses its first row as the shared camera model.
+Supplied-intrinsic adapters also exist for VBR and LaMAria. RGB-only mode
+estimates one shared camera model.
+
+Stereo takes a synchronized, rectified right-image PNG inventory and a positive
+`stereo_baseline_m`, with a fixed horizontal rig translation. The public
+configuration does not expose an arbitrary stereo extrinsic matrix. Metric
+camera-Z depth is predicted internally by MoGe-2 ViT-L without a normal head,
+available as `Ruicheng/moge-2-vitl`.
+
+**Other Properties Related to Input:**
+
+- Training uses 15-frame clips resized while preserving aspect ratio and
+  center-cropped to 480 x 640. Inference raster size depends on the protocol;
+  the canonical KITTI input is 280 x 504, and the 7Scenes trajectory
+  protocol uses 434 x 574. The shipped KITTI and VBR configurations use
+  280 x 504; Oxford uses 378 x 518. The 7Scenes evaluation uses a separate
+  workflow; this repository does not include a 7Scenes adapter.
+- Images and camera intrinsics must undergo consistent resize and crop
+  transforms. Stereo requires synchronization, rectification, and a correct
+  horizontal baseline consistent with the processed images.
+- RGB-only calibration estimates intrinsics from a finite prefix, fixes the
+  camera model, and replays that prefix before continuing online. Our
+  evaluation initializes with the first 32 frames, and robust128
+  calibration uses 128 selected keyframes. The repository exposes configurable
+  calibration budgets. A shared focal correction is optimized while the
+  principal point remains fixed.
+- The implementation's metric-depth validity check requires a valid provider
+  mask, finite depth, and camera-Z depth in the inclusive range 0.2-500 m.
+  If a full-resolution prediction, its registered stride-4 depth raster, or
+  all sampled patch centers contain no valid depth, the sequence terminates
+  and the run cannot commit a successful complete trajectory. Failed runs may
+  leave cache or older output files; use stage validation to establish success.
+- `max_frames=0` uses the full configured inventory. The default DPVO frame
+  buffer has capacity 32,768; buffer exhaustion raises an error. The bounded
+  active optimization window does not make sequence duration or total cached
+  data unlimited. The shared-intrinsic model assumes consistent camera geometry.
+
+## Output:
+
+**Output Type(s):** Per-frame camera trajectories, intrinsics, cached frame-level
+metric depth and provider masks, and processing/audit metadata.
+
+**Output Format(s):** Trajectory NPZ files with accompanying JSON records.
+The frontend cache stores raw `depth.f32` and `mask.u8` arrays with JSON contract,
+progress, and successful-completion records. It does not export dense point
+clouds, meshes, or sparse patch geometry.
+
+**Output Parameters:** `c2w` and inverse `w2c` have shape `[N,4,4]`;
+`intrinsics` has shape `[N,4]`. The trajectory includes `frame_ids`,
+`source_frame_ids`, `input_hw`, and stereo metadata. Poses refer to `cam0`, the
+left camera for stereo. Cached camera-Z depth is FP32 `[N,H,W]`; the raw provider
+mask is `uint8 [N,H,W]`. Consumers must also check finite depth and range;
+the cached mask alone is not the final validity test.
+
+KITTI local predictions are written under `output_root/local/inference/`;
+other adapters use `output_root/local/inference/predictions/`. The separate
+loop-correction command writes optional LC predictions under
+`output_root/LC/inference/` and also retains `local_c2w` and the loop-edge count.
+Depth caches are under
+`output_root/frontend-cache/<dataset>/<case>/`.
+
+Dense geometry is assembled by back-projecting camera-Z depth with intrinsics
+and transforming it by `c2w`. The CLI outputs the depth and poses needed for
+this operation but does not export dense reconstructions. Metric scale remains
+an estimate. Learned correspondence weights should not be interpreted as
+calibrated pose or depth uncertainty.
+
+**Other Properties Related to Output:** Output length follows the processed
+frame inventory. Trajectories use meters for translation and homogeneous
+camera-to-world/world-to-camera matrices; cached depth uses meters along the
+camera Z axis. Depth predictions are valid only where the provider mask is
+valid, values are finite, and depth is in the inclusive range 0.2-500 m.
+The pipeline raises an error for frames with no usable depth priors. Check
+successful-completion records before consuming outputs; failed runs can retain
+intermediate caches or older files. The model does not provide calibrated
+pose/depth uncertainty or a guarantee of geometric correctness.
+
+Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated
+systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks
+(e.g., CUDA libraries), the model achieves faster training and inference times
+compared to CPU-only solutions. MetricTrail requires CUDA for the provided
+pipeline; its reported benchmarks do not include a measured CPU comparison.
+
+## Software Integration:
+
+**Runtime Engine(s):** PyTorch/CUDA. The documented environment is Python 3.10,
+PyTorch 2.5.1 and CUDA 12.4, with xformers for MoGe-2 attention, yacs, OpenCV,
+Pillow, and SciPy. Separately invoked loop correction uses NumPy/SciPy on CPU,
+with no PyTorch/CUDA, PyPose, FAISS or SALAD runtime dependency. The package
+metadata permits broader versions, but those combinations have not been validated.
+No precompiled DPVO CUDA extensions are included. Before tracking, build
+`cuda_corr`, `cuda_ba`, and `lietorch_backends`
+from source in the inference environment, using a compatible C++ compiler,
+the CUDA toolkit including `nvcc`, and Eigen 3.4.0 headers. See
+[environment instructions](../README.md#environment)
+and [package dependencies](../pyproject.toml).
+
+**Supported Hardware Microarchitecture Compatibility:**
+
+NVIDIA Ampere (A100)
+
+A CUDA device is required. Compatibility with other GPUs and a minimum memory
+requirement have not been established.
+
+**Supported Operating System(s):** Linux x86_64 is the documented tracking
+environment. DPVO CUDA extensions must be built from source before tracking.
+Validation results for other environments are not documented.
+
+Run with `PYTHONNOUSERSITE=1`. The primary command is
+`python -m metrictrail.cli --config <config.json>`; the default device is
+`cuda:0`. `--case`, `--resume`, `--preflight`, and
+`--stop-after {calibration,local}` control selection and stage execution.
+Preflight checks external files and relevant runtime imports; it does not
+validate model quality. See [running instructions](../README.md#running).
+
+The main pipeline ends at the local trajectory with `enable_lc: false`.
+Run optional loop correction separately on CPU with
+`python -m metrictrail.lc_cli --config <config.json> --case <case> --candidate-file <candidates.json> --candidate-sha256 <sha256>`.
+MetricTrail consumes the supplied candidate pairs; it does not perform place
+retrieval or launch an external retrieval tool.
+
+Validate the implementation, camera model, data preprocessing, output
+conventions, failure handling, and use-case performance before integrating
+the system into a larger application. A measured benchmark memory footprint
+does not by itself establish a supported hardware requirement.
+
+The integration of foundation and fine-tuned models into AI systems requires
+additional testing using use-case-specific data to ensure safe and effective
+deployment. Following the V-model methodology, iterative testing and validation
+at both unit and system levels are essential to mitigate risks, meet technical
+and functional requirements, and ensure compliance with safety and ethical
+standards before deployment.
+
+## Model Version(s):
+
+**MetricTrail 1.0:** The registered research model release. Integrate this version
+by preparing RGB frame inventories and a JSON configuration, obtaining the
+separately distributed MetricTrail checkpoint and compatible upstream weights
+under their respective terms, and invoking
+`python -m metrictrail.cli --config <config.json>`. The caller consumes validated
+trajectory NPZ files, JSON completion records, and optional depth/mask caches,
+and evaluates them for its intended research use.
+
+**Checkpoint:** The multi-dataset-adapted DINO-DPVO checkpoint will be released
+separately on Hugging Face. Place it at `ckpt/dino_dpvo_multidataset15k.pth`
+or set `paths.dino_dpvo_checkpoint` to its local path. It is not included in the
+source repository. **Source revision:**
+`7ead0eca3f8a6a1266a21974c8077cc53940ccf5`.
+Checkpoint SHA256:
+`d0aa57e165b159fc84bdb9d94918ad9b508c153acfb4c46f5058ab80421fb5f5`.
+The checkpoint records format version 1 and training phase `joint`.
+The Python package version `0.1.0` is a separate software version.
+
+The frozen DINOv3 backbone is excluded from the DINO-DPVO checkpoint and must be
+obtained separately as `dinov3_vits16_pretrain_lvd1689m-08c60483.pth`.
+Its validated SHA256 is
+`08c60483bc63c04f533611e34bf70b120eedb7240f469bc16e9e20bf344b941d`.
+MoGe-2 and optional calibration and RoMa dependencies are also separate
+downloads; see the [external-file layout](../README.md#layout-of-external-files).
+Optional loop-retrieval tools are obtained and run independently to generate
+the candidate files consumed by the loop-correction command.
+
+The released configuration supports monocular/stereo tracking and optional
+revisit focal refinement. Core configs disable loop closure. The separate
+postprocessor uses externally supplied identity-edge candidates, with
+VBR-specific residual scales and default scales for other datasets.
+The new postprocessor has not been benchmark-validated.
+Native DPVO loops and global BA are rejected by the configuration
+schema. Focal refinement performs one coarse matching attempt, keeps the stage-A
+focal if refused, and reruns tracking after an accepted correction. Other
+global-BA/refinement variants evaluated below are not exposed by this CLI.
+
+## Training, Testing, and Evaluation Datasets:
+
+### Training Dataset:
+
+#### Data Modality:
+
+- Video
+- Other: Camera-pose and induced patch-motion supervision
+
+#### Training Data Size:
+
+**Video Training Data Size:** Less than 10,000 hours of unique MetricTrail
+fine-tuning video. This excludes repeated sampling
+and the inherited pretraining of DPVO, DINOv3, MoGe-2, and optional components.
+
+Training, including warmup, uses the following sampled clip exposures.
+The table counts clip exposures; unique sequence and image counts and full
+upstream dataset sizes are not shown. Each clip contains 15 frames.
+
+| Dataset | Sampled clip exposures | Source content |
+| --- | ---: | --- |
+| TartanAir | 56,000 | Synthetic simulated environments |
+| TartanGround | 46,000 | Synthetic ground-robot environments |
+| CarlaOcc | 46,000 | Synthetic driving scenes |
+| LightwheelOcc | 46,000 | Synthetic driving scenes |
+| OmniWorld-Game | 46,000 | Game-scene video; only the Game subset |
+| Total | 240,000 | Five project-specific training sources |
+
+**Camera-Pose Supervision Data Size:** 240,000 clips with 15 camera poses per
+clip, totaling 3,600,000 frame-level pose exposures.
+
+**Induced Patch-Motion Supervision Data Size:** The same 240,000 clips, with
+80 sampled source patches per frame (1,200 per clip), totaling 288,000,000
+sampled patch exposures. Motion targets are derived from camera poses, depths,
+and intrinsics.
+
+These are sampled training exposures, including warmup and joint fine-tuning,
+not unique annotations. Patch exposures count sampled source patches; valid
+patch-to-frame motion targets and recurrent reuse are not counted separately.
+
+**Data Collection Method by dataset:** Synthetic. Existing datasets: simulation
+for TartanAir, TartanGround, CarlaOcc, and LightwheelOcc; game capture for
+OmniWorld-Game.
+
+**Labeling Method by dataset:** Synthetic. The local estimator uses the original DPVO
+pose and induced patch-motion supervision. Simulation/game-derived geometry
+and camera metadata supply the supervision. The per-source annotation manifests
+have not been verified.
+
+Images are resized with preserved aspect ratio and center-cropped to
+480 x 640. No training clip exceeds 15 frames, and no evaluation sequence
+or dedicated long-trajectory pose corpus is used. A fine-tuning validation
+split, exact downloaded revisions, acquisition dates, unique example count,
+and complete sample-level provenance are not documented.
+
+The initialized DPVO checkpoint and frozen DINOv3 and MoGe-2 components
+carry additional upstream pretraining history. DINOv3-S/16 was
+pretrained on LVD-1689M. The five-source fine-tuning list does not describe
+all data used to train these upstream components.
+
+**Other Dataset Properties:** Fine-tuning uses synthetic simulation/game scenes,
+RGB video, camera poses, and induced patch-motion supervision. Dataset content
+and assets retain their providers' terms. The fine-tuning data contain no real
+personal information and satisfy applicable privacy requirements and
+dataset-review conditions. This applies to the five fine-tuning datasets;
+the personal-data content of externally pretrained components is not specified.
+A sample-level provenance inventory is not included.
+Linguistic characteristics are Not Applicable to the geometric training task:
+no text prompt, natural-language output, or language-specific evaluation is used.
+
+### Testing Dataset:
+
+We evaluate MetricTrail on the following existing captured benchmarks. Testing
+and quantitative evaluation refer to these benchmark protocols; a separate
+project-specific held-out test partition is not specified.
+
+| Benchmark | Reported coverage and input | Task |
+| --- | --- | --- |
+| KITTI Odometry | Sequences 00-10, all frames; monocular or calibrated stereo | Long-sequence trajectories |
+| Oxford Spires | Ten sequences, 38,380 frames, stride 1 | Trajectories and dense reconstruction |
+| VBR | All seven sequences, stride 1; 8,815-18,846 frames per sequence, routes up to 5.20 km | Long-sequence trajectories |
+| 7Scenes | Dense-reconstruction protocol; additional trajectory evaluation on 18 official test sequences at stride 5 | Indoor geometry and trajectories |
+| TUM-Dynamic | Captured sequences with dynamic content; complete sequence/frame counts are not specified in the main protocol | Dense reconstruction |
+| KITTI video depth | 13 videos, 1,269 frames | Frame-level depth |
+| TUM RGB-D | All nine Freiburg1 sequences, 8,694 associated frames, stride 1 | Trajectories |
+| LaMAria | Five full sequences: R_01, R_04, R_06, R_07, R_08; monocular grayscale, no dataset-specific fine-tuning | Out-of-domain trajectories |
+| Wild-SLAM MoCap | All ten dynamic sequences, 9,039 associated RGB frames, stride 1 | Trajectories with dynamic content |
+
+**Data Collection Method by dataset:** Automatic/Sensors. Existing captured
+camera/sensor datasets.
+
+**Labeling Method by dataset:** Hybrid: Automatic/Sensors, Automated.
+Benchmark-provided reference trajectories,
+geometry/depth, and calibration are used for comparison as applicable.
+
+**Other Dataset Properties:** These are captured RGB or grayscale camera
+sequences, with benchmark-specific depth, calibration, and reference trajectories.
+The coverage and available frame counts are listed above.
+Indoor, outdoor, driving, handheld, and dynamic scenes may
+include identifiable people, private interiors, readable text, or locations.
+Benchmark footage retains its providers' terms. Linguistic characteristics are
+Not Applicable to the evaluated geometric tasks; incidental text in frames is
+not a language input or a language-performance benchmark.
+
+Additional in-the-wild reconstructions are qualitative examples and do not
+constitute a quantified, representative deployment test set.
+
+### Evaluation Dataset:
+
+The results below use research evaluation workflows. The repository provides
+prediction generation but does not include benchmark evaluation scripts.
+Its shipped configuration does not expose the indoor global-BA/refinement
+variants or dense export workflow used in these evaluations.
+
+Trajectory accuracy is full-sequence absolute trajectory error (ATE), measured
+as RMSE after a single proper Sim(3) alignment per sequence. Main long-sequence
+results use meters; additional indoor/dynamic trajectory results use centimeters.
+Dataset averages give sequence-level results equal weight in the reported
+additional protocols. LC denotes the original SALAD/PyPose loop-closure workflow
+used in our evaluations. These results do not establish numerical or benchmark
+equivalence for the new external-candidate NumPy/SciPy postprocessor. See
+[the loop-correction workflow](../docs/loop-closure.md).
+
+| Benchmark / configuration | Average ATE |
+| --- | ---: |
+| KITTI Odometry, RGB-only calibration, without LC | 14.05 m |
+| KITTI Odometry, RGB-only calibration, with LC | 9.78 m |
+| KITTI Odometry, supplied intrinsics, with LC | 7.39 m |
+| KITTI Odometry, calibrated stereo, with LC | 3.00 m |
+| Oxford Spires, RGB-only calibration, without / with LC | 1.77 / 1.77 m |
+| VBR, RGB-only calibration with revisit focal refinement, without / with LC | 16.36 / 10.88 m |
+| 7Scenes, RGB-only calibration, local / with global BA | 6.7 / 6.3 cm |
+| TUM RGB-D, RGB-only calibration, local / global BA / global BA plus refinement | 8.4 / 4.9 / 4.4 cm |
+| TUM RGB-D, supplied intrinsics, local / global BA / global BA plus refinement | 7.5 / 4.1 / 3.7 cm |
+| LaMAria, RGB-only calibration / supplied intrinsics | 8.73 / 8.58 m |
+| Wild-SLAM MoCap, RGB-only calibration / supplied intrinsics | 2.70 / 1.65 cm |
+
+VBR aggregate results include an optional **retrospective** revisit-based focal
+refinement. Under matched settings, this improves base-calibration ATE from
+33.51 to 16.36 m without LC and from 27.95 to 10.88 m with LC. Refinement
+averages 7.1 minutes per routed sequence, dominated by RGB correspondence
+matching. These results should not be described as end-to-end online results
+at the KITTI steady-state throughput.
+
+Dense reconstruction uses RGB-estimated intrinsics without LC. Results are
+evaluated after sequence-level Sim(3) alignment followed by point-to-point ICP.
+CD is symmetric Chamfer distance; F1 is measured at the indicated distance
+threshold.
+
+| Benchmark | CD (m, lower is better) | F1 (%, higher is better) | Threshold |
+| --- | ---: | ---: | --- |
+| 7Scenes | 0.04 | 97.60 | 0.25 m |
+| TUM-Dynamic | 0.13 | 89.58 | 0.25 m |
+| Oxford Spires | 0.98 | 95.12 | 4 m |
+
+KITTI video-depth evaluation fits one multiplicative scale per video without
+a shift. Abs Rel is **0.057** and delta-1 accuracy
+(depth-ratio error below 1.25) is **97.8%**. Dense depth comes from the frozen
+frame-level provider; it is not a densely optimized output of local BA.
+
+**Data Collection Method by dataset:** Automatic/Sensors. The captured benchmarks
+described under
+[Testing Dataset](#testing-dataset).
+
+**Labeling Method by dataset:** Hybrid: Automatic/Sensors, Automated.
+Reference-trajectory, geometry, and depth
+comparison under the alignment protocols above. Sim(3), ICP, and depth-scale
+alignment measure agreement after alignment; these scores do not establish
+unaligned metric-scale accuracy, calibrated uncertainty, demographic parity,
+or safe downstream control.
+
+**Other Dataset Properties:** Quantitative evaluation uses the same benchmark
+coverage, data counts, modalities, scene content, and sensor-derived references
+listed under [Testing Dataset](#testing-dataset). The separate tasks are
+trajectory estimation, dense reconstruction, and frame-level depth, under the
+alignment protocols above. Exact TUM-Dynamic counts and the dense-reconstruction
+7Scenes frame inventory are not documented; the 18-sequence
+7Scenes count refers to the additional trajectory protocol. No separate
+evaluation dataset with language inputs is used, so linguistic characteristics
+are Not Applicable. Captured footage may contain personal or identifying scene
+information and retains its providers' terms.
+
+### Inference:
+
+**Acceleration Engine:** PyTorch/CUDA with DPVO CUDA extensions and xformers
+attention for MoGe-2. The default configuration enables FP16 for MoGe-2 and a
+depth-overlap queue of four. DINOv3 feature extraction uses BF16; the trained
+checkpoint stores FP32 tensors. No TensorRT or Triton export is provided.
+
+**Test Hardware:** NVIDIA Ampere A100. The inference GPU's memory capacity and a
+minimum supported hardware configuration are not specified.
+
+The following measurements use KITTI-02. Latency means
+time to first output; throughput means steady-state processing; memory means
+peak allocated GPU memory.
+
+| Configuration | First-output latency | Steady-state throughput | Peak GPU memory |
+| --- | ---: | ---: | ---: |
+| Supplied intrinsics | Approximately 0.7 s | 12.70 FPS | 3.21 GB |
+| RGB-only calibration | Approximately 175 s | 12.70 FPS | 6.81 GB |
+
+RGB-only startup includes initial MoGe-2 camera prediction, geometric
+calibration, and prefix replay. Memory subsequently drops to 3.21 GB.
+These measurements are specific to the reported KITTI configuration and do
+not include a general latency guarantee for optional retrospective refinement.
+
+Known limitations include dependence on depth-provider accuracy, degraded
+sparse correspondence under low parallax, rapid rotation, rolling shutter,
+and motion blur, and sensitivity to the shared camera model. Dynamic-scene
+reconstruction improvements are not uniform: the TUM-Dynamic scores trail
+leading evaluated methods. MetricTrail uses no explicit
+dynamic-object mask or dynamics-specific objective. Optional retrieved loop
+edges assume near-duplicate views are approximately coincident without
+pairwise geometric verification; incorrect matches can affect the trajectory.
+
+## Ethical Considerations:
+
+NVIDIA believes Trustworthy AI is a shared responsibility and we have established
+policies and practices to enable development for a wide array of AI applications.
+Developers should work with their internal model team to ensure this model meets
+requirements for the relevant industry and use case and addresses unforeseen
+product misuse.
+
+Please make sure you have proper rights and permissions for all input image and
+video content; if image or video includes people, personal health information,
+or intellectual property, the image or video generated will not blur or maintain
+proportions of image subjects included.
+
+Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns
+[here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
+