| --- |
| language: |
| - en |
| tags: |
| - 3d |
| - depth |
| - segmentation |
| - voxel-occupancy |
| pretty_name: PartProbe3D compact reproduction data |
| --- |
| |
| # PartProbe3D reproduction data |
|
|
| This release supports SS, generative SLAT segmentation, and 32³ SLAT shape training. |
| The default transfer is **271.79 GB**, including observations, SS targets, and available shape targets. |
| Default downloads exclude optional and deprecated folders. |
|
|
| <!-- partprobe3d-required-files:start --> |
| ## Mandatory files |
|
|
| These paths refer to the Hugging Face repository root. |
| Files are mandatory for the task specified in each row. |
| The default command restores training files. |
| The benchmark also requires the separate download with `--dataset evaluation`. |
|
|
| | File or directory | Required task | Contents | |
| | --- | --- | --- | |
| | `training/index.json` | Training restoration | Component paths, sizes, monitoring counts, and source identities | |
| | `training/observations/<dataset>/` | Training on the selected dataset | Archive index, sample NPZ files, calibration, object mappings, and scene voxel targets | |
| | `training/ss/<dataset>/` | Direct SS training after target publication | Archive index, native SS latents, and codec provenance | |
| | `training/shape/32/<dataset>/` | SLAT shape training where targets are available | Archive index and object shape targets with explicit exclusions | |
| | `evaluation/index.json` | Benchmark restoration | Archive list and SHA-256 checksums | |
| | `evaluation/metadata.tar` | Benchmark evaluation | Portable manifests, source selections, and dataset documentation | |
| | `evaluation/picasso-*.tar` | Benchmark evaluation | All 100 PICASSO images, masks, previews, and GT meshes | |
| | `evaluation/messykitchen-*.tar` | Benchmark evaluation | All 1,503 MessyKitchens images, masks, previews, and 100 shared GT meshes | |
| | `evaluation/3dfront-*.tar` | Benchmark evaluation | The selected 1,000 3D-FRONT test images, masks, previews, and GT meshes | |
|
|
| Restore every archive listed in the selected component's `index.json`. |
| The evaluation bundle has 33 mandatory archives and requires 29.313 GB. |
| Older checkouts also require the corresponding restore patch under `code/`. |
| <!-- partprobe3d-required-files:end --> |
|
|
| <!-- partprobe3d-tasks:start --> |
| ## Download by task |
|
|
| Select a task to avoid unnecessary data transfers. |
| The [task catalog](tasks/index.json) records component paths, sizes, and the data revision. |
| The [task guide](tasks/README.md) lists downloads for individual datasets. |
|
|
| | Task | Required data | Default transfer, GB | |
| | --- | --- | ---: | |
| | [`eval`](tasks/eval/index.json) | RGB, masks and GT meshes | 29.313 | |
| | [`ss`](tasks/ss/index.json) | Observations and native SS targets | 265.667 | |
| | [`slat-seg`](tasks/slat-seg/index.json) | Observations only | 221.950 | |
| | [`slat-shape`](tasks/slat-shape/index.json) | Observations and 32³ object shape targets | 233.780 | |
| | [`all-training`](tasks/all-training/index.json) | All published training components | 277.497 | |
|
|
| Use the current project checkout for these commands. |
| Preview the transfer before downloading: |
|
|
| ```bash |
| python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --task ss --dry-run |
| ``` |
|
|
| Select the task required by this server: |
|
|
| ```bash |
| python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --task eval |
| python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --task ss |
| python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --task slat-seg |
| python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --task slat-shape |
| ``` |
|
|
| Select individual datasets with `--dataset`, using commas for multiple datasets. |
|
|
| ```bash |
| python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --task eval --dataset picasso |
| python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --task eval --dataset picasso,messykitchen |
| python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --task slat-seg --dataset foundationpose |
| ``` |
|
|
| Task defaults follow the published component inventory. |
| Explicit shape requests require matching object targets before archive transfer. |
|
|
| SS uses the published native targets without another target encoding pass. |
| Segmentation computes its instance codes and image features during training. |
|
|
| Online shape conditioning avoids a separate image-feature cache. |
|
|
| Evaluation metadata excludes the project README. Dataset provenance documents remain in the archive. |
|
|
| Model weights remain separate. Download only the assets required by the selected model. |
|
|
| Old commands without `--task` still restore all training components. |
| <!-- partprobe3d-tasks:end --> |
|
|
| ## Restore tools |
|
|
| Use project Git revision `76437eb951160603d0bfd4797212094aea67a4fa` or later. |
| This revision includes the restore tools and the online SS defaults. |
|
|
| ```bash |
| python -m pip install -e '.[preprocess,ss-multiobj-depth,slat-segvigen,slat-multiobj-shape]' |
| ``` |
|
|
| ## Default download |
|
|
| Run this command from the patched project root: |
|
|
| ```bash |
| python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed |
| ``` |
|
|
| The command restores observations and SS targets for 3D-FRONT, SAGE-10k, and FoundationPose. |
| It also restores available 32³ object shape targets. |
| It verifies each archive and removes temporary archives after restoration. |
| The downloader pins one Hub revision before reading any archive. |
| Add `--revision <Hub commit>` to select a fixed release. |
| Add `--keep-archives` only when you need temporary transfer files. |
|
|
| | Dataset | SS/segmentation training views | Shape training views | Monitoring views | Transfer, GB | |
| | --- | ---: | ---: | ---: | ---: | |
| | 3dfront | 39,930 | 39,930 | 16 | 123.01 | |
| | sage | 17,456 | 17,453 | 16 | 78.73 | |
| | foundationpose | 42,256 | 42,256 | 16 | 75.75 | |
|
|
| The default includes FoundationPose for SS and segmentation. |
| FoundationPose shape targets remain unavailable. |
| Use this command to restore FoundationPose alone: |
|
|
| ```bash |
| python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --dataset foundationpose |
| ``` |
|
|
| ## Contents |
|
|
| Sample NPZ files retain RGB, metric depth, visible labels, complete object occupancy, and the original transforms. |
| Calibration, scene voxel targets, object mappings, and original extent manifests remain available. |
| The compact export omits separate RGB, masks, depth copies, overlays, and duplicate voxel exports. |
| Every training view remains available to SS and segmentation. |
| The first 16 test views per dataset support existing eight-view and sixteen-view training monitors. |
| All other test observations require an optional download. |
|
|
| Object shape arrays retain their original values and identities. |
| Shape manifests explicitly exclude the three failed SAGE views used by the existing experiment. |
| SS and segmentation retain those views. |
| The export omits combined scene latents because the current shape objective uses object targets. |
| Model weights and image-feature caches remain separate dependencies. |
|
|
| ## Optional files |
|
|
| These files support additional datasets, test views, and target generation. |
| The mandatory evaluation bundle already includes its RGB images, masks, and GT meshes. |
| Some SS configurations require the matching optional occlusion component. |
|
|
| | File or directory | Purpose | Transfer, GB | |
| | --- | --- | ---: | |
| | `optional/evaluation/ycbv/` | YCB-V mesh evaluation | 1.670 | |
| | `optional/geometry/3dfront/` | Mesh scoring and new target encoding | 6.252 | |
| | `optional/geometry/sage/` | Mesh scoring and new target encoding | 303.693 | |
| | `optional/held_out/3dfront/` | Remaining test observations | 22.440 | |
| | `optional/held_out/foundationpose/` | Remaining test observations | 6.825 | |
| | `optional/held_out/sage/` | Remaining test observations | 8.183 | |
| | `optional/occlusion/3dfront/` | GT relations for SS object attention | 0.240 | |
| | `optional/occlusion/sage/` | GT relations for SS object attention | 0.103 | |
|
|
| Restore observations before geometry, occlusion, or test overlays. |
| YCB-V evaluation includes its own observations and geometry. |
| Select each optional component explicitly: |
|
|
| ```bash |
| python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --dataset sage --optional geometry |
| python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --dataset 3dfront --optional occlusion |
| python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --dataset sage --optional held-out |
| python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --dataset ycbv --optional evaluation |
| ``` |
|
|
| ## Train all three stages |
|
|
| Obtain the configured model weights separately. |
| The restored SS targets support online SS training directly. |
|
|
| ```bash |
| export CUDA_VISIBLE_DEVICES=4,6 |
| export CUDA_DEVICE_ORDER=PCI_BUS_ID |
| python -m ss_multiobj_depth.train |
| python -m slat_segvigen.train --config slat_segvigen/configs/object_mean_image_nora_coverage_gate.json |
| python -m preprocess.slat.prepare --config preprocess/slat/configs/front3d_sage_32_assigned_object.json --stage features --devices cuda:0 cuda:1 |
| python -m slat_multiobj_shape.train --config slat_multiobj_shape/configs/front3d_sage_32_assigned_object.json |
| ``` |
|
|
| Use the saved configuration and pretrained assets for a specific experiment. |
| Feature preparation creates local caches without meshes or another dataset download. |
| For lower local storage, generate an online shape configuration: |
|
|
| ```bash |
| python -m preprocess.reproduction configure-shape --output outputs/reproduction/shape_online.json |
| python -m slat_multiobj_shape.train --config outputs/reproduction/shape_online.json |
| ``` |
|
|
| The generated configuration computes image conditioning during training and avoids separate image-feature caches. |
| It preserves the selected preset's model and optimization settings. |
| Use the original cached preset when reproducing a cached-feature experiment. |
| Data and decoder provenance remain in the restored manifests. |
| Exact checkpoint resume requires the original matching feature and data identities. |
|
|
| ## Deprecated uploads |
|
|
| `_deprecated/20261020/` contains superseded bundles and catalogs. |
| The project downloader never selects these paths. |
| An unfiltered Hugging Face snapshot download includes every folder. |
| Use the project downloader for minimal transfer. |
| The [original release](https://huggingface.co/datasets/cubec/PartProbe3D-preprocessed/tree/d13e577a7e71291ed94fa40e4297f54106577fbd) remains available by revision. |
|
|
| ## Source terms and previews |
|
|
| Source dataset terms continue to apply to these derived observations. |
| The prepared data does not replace access agreements for original source assets. |
|
|
|  |
|
|
|  |
|
|
| [Posed object geometry](previews/3dfront/train-geometry.glb) |
|
|
| <!-- partprobe3d-frozen-evaluation:start --> |
| ## Required evaluation dataset |
|
|
| The required [evaluation dataset](evaluation/README.md) contains 2,603 images with visible masks and GT object meshes. |
| It includes all 100 PICASSO images, all 1,503 MessyKitchens images, and 1,000 random 3D-FRONT test views. |
| The PartProbe3D benchmark requires this dataset. |
| Benchmark inference and scoring require geometry meshes. |
| Model comparisons preserve native geometry and omit independent texture generation. |
| The transfer is 29.313 GB. |
| MessyKitchens shares 100 GT scene meshes across its views. |
| MessyKitchens uses reviewed GT-assisted SAM3 masks for all 1,503 views. |
| The bundle retains exact independent masks, registration records, and all 13 audit flags. |
| Native SAM3 quality resolves overlap for evaluation labels, with smaller object IDs breaking ties. |
| See the [mask release](https://github.com/cube-c/PartProbe3D/blob/main/reports/messykitchen_registered_release_20261024.md) for the pinned revision and migration paths. |
| Default training downloads exclude this bundle. |
|
|
| Use the current project checkout for the task commands. |
|
|
| ```bash |
| python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --task eval |
| ``` |
| <!-- partprobe3d-frozen-evaluation:end --> |
|
|
| <!-- partprobe3d-ss-targets:start --> |
| ## Encoded SS targets |
|
|
| The standard download includes encoded SS targets for the datasets listed below. |
| These targets remove GPU encoding from the restore procedure. |
| They retain the original cache manifests, latent tensors, and monitoring targets. |
| Their native latent shape is `8×16×16×16`. |
| Model weights remain separate dependencies. |
|
|
| | Dataset | Target views | Transfer, GB | |
| | --- | ---: | ---: | |
| | 3dfront | 49,996 | 31.249 | |
| | sage | 19,408 | 4.175 | |
| | foundationpose | 46,948 | 8.292 | |
|
|
| Use the current project checkout and download the selected datasets: |
|
|
| ```bash |
| python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --task ss |
| ``` |
|
|
| SS training can then use the default online configuration without a preparation command. |
| The downloader checks archive hashes before extraction. |
| The training loader checks dataset and frozen codec identities. |
| The image encoders compute features during training. |
| <!-- partprobe3d-ss-targets:end --> |
|
|
| <!-- partprobe3d-scenes:start --> |
| ## Voxel-aligned scene latents |
|
|
| These optional caches contain one token per saved reference voxel. |
| Overlapping objects share that token and retain separate membership labels. |
| The `latent_valid` flag identifies cells with a native encoder output. |
| Invalid cells retain their reference coordinates and contain zero latent values. |
| The archives preserve the original NPZ files, calibration transforms, and codec provenance. |
|
|
| | Dataset | Grid | Training views | Transfer, GB | |
| | --- | ---: | ---: | ---: | |
| | [3dfront](optional/scene/32/3dfront/index.json) | 32³ | 39,930 | 4.590 | |
| | [sage](optional/scene/32/sage/index.json) | 32³ | 17,456 | 1.176 | |
| | [foundationpose](optional/scene/32/foundationpose/index.json) | 32³ | 42,256 | 5.046 | |
| | [3dfront](optional/scene/64/3dfront/index.json) | 64³ | 39,778 | 19.744 | |
| | [sage](optional/scene/64/sage/index.json) | 64³ | 17,365 | 4.252 | |
| | [foundationpose](optional/scene/64/foundationpose/index.json) | 64³ | 42,223 | 19.809 | |
| | [omni6dpose](optional/scene/64/omni6dpose/index.json) | 64³ | 44,788 | 5.469 | |
|
|
| Restore the matching observation bundles before reading scene targets. |
| The `slat-seg` task downloads those observations without SS or object latent targets. |
|
|
| ```bash |
| python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --task slat-seg |
| ``` |
|
|
| Restore the selected scene caches: |
|
|
| ```bash |
| python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --dataset 3dfront --bundle-root optional/scene/32 --cleanup |
| python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --dataset sage --bundle-root optional/scene/32 --cleanup |
| python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --dataset foundationpose --bundle-root optional/scene/32 --cleanup |
| python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --dataset 3dfront --bundle-root optional/scene/64 --cleanup |
| python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --dataset sage --bundle-root optional/scene/64 --cleanup |
| python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --dataset foundationpose --bundle-root optional/scene/64 --cleanup |
| python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --dataset omni6dpose --bundle-root optional/scene/64 --cleanup |
| ``` |
|
|
| The restored caches reside under `datasets/<dataset>/slat/<resolution>/scene_targets/`. |
| `dataloader.slat_scene.SceneTargetDataset` reads them with their matching saved observations. |
| Raw meshes and source datasets are unnecessary for this reader. |
| Scene downloads remain separate from object targets and default task downloads. |
| <!-- partprobe3d-scenes:end --> |
|
|
| ## Deprecated MessyKitchens masks |
|
|
| Historical MessyKitchens mask archives now reside under [_deprecated/messykitchen/original_masks/evaluation](https://huggingface.co/datasets/cubec/PartProbe3D-preprocessed/tree/main/_deprecated/messykitchen/original_masks/evaluation). |
| Current evaluation downloads exclude these archives. |
|
|
| <!-- partprobe3d-omni6dpose:start --> |
| ## Omni6DPose SOPE |
|
|
| This release contains 3 selected frames per SOPE sequence. |
| It contains 55,848 usable views from 18,617 sequences. |
| The visibility requirement excludes 3 source views. |
| The official splits contain 44,788 training views and 11,060 test views. |
| All selected frames use one dataset and the same component paths. |
|
|
| Every retained mask covers strictly more than 0.1% of the native image. |
| At 640 by 480 pixels, each retained object covers at least 308 pixels. |
| Each view contains at least two retained objects. |
| The observation, object, SS, and scene components use this selection. |
|
|
| Native RGB, perfect metric depth, instance labels, and calibration come from SOPE. |
| The observation bundle stores these arrays in sample NPZ files. |
| Targets contain surface voxel occupancy at resolution 64. |
| PointCloudExtent uses near and far trim fractions of 0.0525, far margin 0.125, and padding 1.31. |
| Object latents and scene latents use resolution 32. |
| SS targets contain native latents with shape `8×16×16×16`. |
|
|
| | Component | Views | Transfer, GB | |
| | --- | ---: | ---: | |
| | [training/observations/omni6dpose](training/observations/omni6dpose/index.json) | 44,804 | 70.945 | |
| | [training/shape/32/omni6dpose](training/shape/32/omni6dpose/index.json) | 44,788 | 1.739 | |
| | [training/ss/omni6dpose](training/ss/omni6dpose/index.json) | 55,848 | 145.935 | |
| | [optional/held_out/omni6dpose](optional/held_out/omni6dpose/index.json) | 11,044 | 17.792 | |
| | [optional/geometry/omni6dpose](optional/geometry/omni6dpose/index.json) | 55,848 | 12.039 | |
| | [optional/scene_mask_area/32/omni6dpose](optional/scene_mask_area/32/omni6dpose/index.json) | 44,788 | 1.573 | |
|
|
| The core observations include all training views and 16 test views for monitoring. |
| The held-out component adds the remaining 11,044 test views. |
| The SS component contains all 55,848 views. |
| Optional geometry contains canonical GLBs and per-frame instance transforms. |
| Filtered scene latents occupy the same namespace as the other filtered datasets. |
|
|
| Restore all training components with this command: |
|
|
| ```bash |
| python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --task all-training --dataset omni6dpose --cleanup |
| ``` |
|
|
| Restore optional components with these commands: |
|
|
| ```bash |
| python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --dataset omni6dpose --bundle-root optional/held_out --cleanup |
| python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --dataset omni6dpose --bundle-root optional/geometry --cleanup |
| python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --dataset omni6dpose --bundle-root optional/scene_mask_area/32 --cleanup |
| ``` |
|
|
| Use project Git revision `513895b85af6e56716e1ed51d9352639a27e8e8d` or a later revision. |
| The committed code patch is available at [code/omni6dpose.patch](code/omni6dpose.patch). |
| Apply this patch to project Git revision `32d92a1e1b9e99eafaac9dda13c43e676a7aeeb3` when necessary. |
| Model weights remain separate from this dataset. |
| Component manifests preserve model hashes, source hashes, and generation settings. |
|
|
| The shape encoder is `TRELLIS.2 shape_enc_next_dc_f16c32_fp16`. |
| Its safetensors SHA-256 is `f37c5ff5b983b68e9946060000f09bc131f3e84318a2c8b7430a81e4b4636c41`. |
| The paired Pixal3D decoder SHA-256 is `e3b718d3e43e4f8780e9a24ac6fff231811a67e3b058e336e10fe654c911d581`. |
|
|
| Preview: [RGB, masks, extent, meshes, and voxels](previews/omni6dpose/extent_voxels.png). |
| The [release inventory](releases/omni6dpose/index.json) records source identities and component checksums. |
| Dataset credit: [Omni6DPose](https://jiyao06.github.io/Omni6DPose/). |
| Source documentation: [Omni6DPoseAPI](https://github.com/Omni6DPose/Omni6DPoseAPI). |
| <!-- partprobe3d-omni6dpose:end --> |
|
|
