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README.md

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+---
+license: other
+license_name: graspnet-1billion-derived
+license_link: LICENSE
+pretty_name: GraspNet v14 + Native-Width Labels
+tags:
+  - robotics
+  - grasping
+  - graspnet
+  - depth
+size_categories:
+  - 100K<n<1M
+---
+
+# GraspNet v14 + Native-Width Labels
+
+Precomputed training labels for **GraspFormer**, derived from
+[GraspNet-1Billion](https://graspnet.net/). This repo contains two label sets,
+resampled/rebuilt into the camera frame for all 190 GraspNet scenes and
+sharded by scene range for convenient streaming.
+
+## Provenance and license
+
+**This is a derivative of GraspNet-1Billion.** It is for **academic,
+non-commercial use only** and **inherits the terms of the original GraspNet
+dataset**. Do **not** use this data commercially. You must obtain the base
+RGB-D images, point clouds, object meshes, and original grasp annotations from
+the official GraspNet source: https://graspnet.net/. This repository only adds
+derived/resampled label tensors that are meaningless without the base dataset.
+
+## Contents
+
+38 shards (19 per dataset), plus `manifest.json` (per-shard byte counts and
+SHA256 checksums) and `SHA256SUMS` (plain-text checksums for all shards).
+Total size: **353,580,811,936 bytes (~329.3 GiB / ~353.6 GB)**.
+
+Each dataset is split into shards of **10 scenes each**, aligned with the
+standard GraspNet splits:
+
+| dataset | split | scenes | shards | bytes |
+|---|---|---|---|---|
+| `interpolation4d_v14` | train | 0000-0099 | 10 | 121,152,250,590 |
+| `interpolation4d_v14` | test_seen | 0100-0129 | 3 | 43,928,534,666 |
+| `interpolation4d_v14` | test_similar | 0130-0159 | 3 | 36,735,407,768 |
+| `interpolation4d_v14` | test_novel | 0160-0189 | 3 | 30,163,615,392 |
+| `native_width_v1` | train | 0000-0099 | 10 | 63,898,562,560 |
+| `native_width_v1` | test_seen | 0100-0129 | 3 | 23,462,502,400 |
+| `native_width_v1` | test_similar | 0130-0159 | 3 | 20,247,879,680 |
+| `native_width_v1` | test_novel | 0160-0189 | 3 | 13,992,058,880 |
+
+`interpolation4d_v14` totals 231,979,808,416 bytes (~216.0 GiB);
+`native_width_v1` totals 121,601,003,520 bytes (~113.2 GiB).
+
+**Known exclusion:** scene 41, view 255 is excluded from both label sets
+upstream — its GraspNet annotation is corrupt.
+
+### `interpolation4d_v14/` — resampled 4D grasp-quality scores
+
+GraspNet's raw grasp-quality labels resampled into each camera view, stored as
+`uint8` tensors of shape `(N, 300, 12, 4)` per view (`N` query points × 300
+view directions × 12 in-plane rotations × 4 gripper depths), alongside the
+corresponding 3D query points and validity masks. Shards are
+zstd-compressed tarballs (`interpolation4d_v14_scenes_XXXX-YYYY.tar.zst`),
+one per 10-scene range.
+
+Extract with:
+
+```bash
+zstd -d < interpolation4d_v14_scenes_XXXX-YYYY.tar.zst | tar -x -C $GRASPNET_SAVE_DIR
+```
+
+### `native_width_v1/` — native feasible-width intervals
+
+Sparse per-view feasible-width intervals computed directly from the ground-
+truth object meshes (not resampled from the original score grid), stored as
+`.npz` files. Shards are plain tarballs
+(`native_width_v1_scenes_XXXX-YYYY.tar`), one per 10-scene range.
+
+Extract with:
+
+```bash
+tar -xf native_width_v1_scenes_XXXX-YYYY.tar -C $GRASPNET_SAVE_DIR
+```
+
+## Verifying downloads
+
+`SHA256SUMS` lists `sha256  filename  bytes` for every shard. After
+downloading, verify with:
+
+```bash
+sha256sum -c SHA256SUMS
+```

SHA256SUMS

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manifest.json

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+ "created": "2026-07-10",
+ "description": "GraspFormer processed labels: interpolation4d_v14 resampled scores (all 190 GraspNet scenes; uint8 (N,300,12,4) per view + point3d/masks) and native_width_v1 feasible width intervals (sparse npz per view; scene 41 view 255 excluded upstream - corrupt GraspNet annotation).",
+ "extraction": "labels: zstd -d < shard.tar.zst | tar -x -C $GRASPNET_SAVE_DIR ; widths: tar -xf shard.tar -C $GRASPNET_SAVE_DIR",
+ "total_bytes": 353580811936,
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