Duplicate from Voxel51/SceneFun3D

Co-authored-by: Harpreet Sahota <harpreetsahota@users.noreply.huggingface.co>

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README.md

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+---
+annotations_creators:
+  - expert-generated
+  - machine-generated
+language:
+  - en
+license: cc-by-nc-sa-4.0
+pretty_name: SceneFun3D
+size_categories:
+  - n<1K
+splits:
+  - train
+  - val
+  - test
+task_categories:
+  - object-detection
+tags:
+  - fiftyone
+  - 3d
+  - point-cloud
+  - fo3d
+  - group
+  - video
+  - rgbd
+  - depth
+  - affordance
+  - functionality
+  - indoor-scenes
+  - robotics
+dataset_summary: >
+  SceneFun3D is a 3D scene-understanding dataset of high-resolution laser-scan
+  point clouds of indoor environments densely annotated with fine-grained
+  functional interactive elements (handles, knobs, buttons, switches, ...), their
+  affordances, motion parameters, and natural-language task descriptions. This is
+  the FiftyOne version: a grouped multimodal dataset where each scene is a group
+  containing the scene's FO3D laser-scan point cloud (with 3D functional elements)
+  plus one video slice per iPad recording of the scene. Video frames carry
+  per-frame depth, camera poses, intrinsics, and the functional elements projected
+  into the frames as 2D boxes + keypoints. This build samples 10 scenes from each
+  of the train/val/test splits (each sample tagged with its split).
+---
+
+# Dataset Card for SceneFun3D
+
+![image/png](scenefun3d.gif)
+
+SceneFun3D is a 3D scene-understanding dataset of high-resolution Faro laser-scan point clouds of indoor environments, densely annotated with fine-grained
+**functional interactive elements** (handles, knobs, buttons, switches, ...), their **affordances**, **motion** parameters, and free-form **task descriptions**.
+Each scene is also captured by several iPad video sequences with RGB, depth, camera poses, and intrinsics.
+
+This is the FiftyOne version of the dataset: a **grouped multimodal** dataset where each **scene** is a group containing the scene's FO3D laser-scan point cloud
+(with 3D functional elements) plus one video slice per iPad recording (`ipad_1`, `ipad_2`, ...). The video frames carry per-frame depth (as `Heatmap` labels),
+camera poses, and intrinsics, and the 3D functional elements are projected into the frames as 2D detections + keypoints, linked back to the 3D boxes via
+`fo.Instance`.
+
+This dataset was created with [FiftyOne](https://github.com/voxel51/fiftyone) and can be loaded and visualized in the FiftyOne App (3D viewer for the point cloud,
+video player for the iPad sequences).
+
+## Installation
+
+```bash
+pip install -U fiftyone
+```
+
+## Usage
+
+Build the dataset (downloads visit + video assets on demand, then parses them):
+
+```bash
+import fiftyone as fo
+from fiftyone.utils.huggingface import load_from_hub
+from huggingface_hub import snapshot_download
+
+
+# Download the dataset snapshot to the current working directory
+
+snapshot_download(
+    repo_id="Voxel51/SceneFun3D", 
+    local_dir=".", 
+    repo_type="dataset"
+    )
+
+# Load dataset from current directory using FiftyOne's native format
+dataset = fo.Dataset.from_dir(
+    dataset_dir=".",  # Current directory contains the dataset files
+    dataset_type=fo.types.FiftyOneDataset,  # Specify FiftyOne dataset format
+    name="SceneFun3D"  # Assign a name to the dataset for identification
+)
+
+# Launch the App
+session = fo.launch_app(dataset)
+
+```
+
+## Dataset Details
+
+### Dataset Description
+
+<!-- Provide a longer summary of what this dataset is. -->
+
+SceneFun3D targets *fine-grained functionality and affordance understanding* in 3D scenes: beyond recognizing objects, it localizes the small interactive parts a
+person actually manipulates (a drawer handle, a light switch, a stove knob) and describes how to interact with them. The full dataset (per the paper) provides
+**more than 14.8k (14,867) functional interactive element annotations across 710 high-resolution real-world indoor scenes**, with **9 Gibsonian-inspired affordance
+categories**, **motion parameters for 14,279 elements** (8,325 translational, 6,542 rotational), and **natural-language task descriptions for 10,913 elements**
+(17,133 including automated rephrasings). Each scene is a combined, 5mm-voxel-downsampled Faro laser scan (several million points); functional elements
+are annotated as point-index masks on that scan.
+
+In this FiftyOne build, every scene becomes one FO3D point cloud, each functional element becomes a 3D `Detection` (axis-aligned box from the masked points) carrying
+its affordance and motion, and each scene's iPad recordings are video slices with the elements projected into the frames (see Dataset Structure).
+
+- **Curated by:** Alexandros Delitzas, Ayca Takmaz, Federico Tombari, Robert
+  Sumner, Marc Pollefeys, and Francis Engelmann (ETH Zurich, Google, TU Munich,
+  Microsoft). Built on top of ARKitScenes.
+  
+- **Funded by:** A Career Seed Award from the ETH Zurich Foundation and an
+  Innosuisse grant (48727.1 IP-ICT); AD supported by a HELLENiQ ENERGY scholarship.
+
+- **Shared by:** SceneFun3D authors (ETH Zurich CVG release mirror).
+
+- **Language(s):** English (task descriptions).
+
+- **License:** Non-commercial research use, inherited from ARKitScenes
+  (CC BY-NC-SA 4.0).
+
+### Dataset Sources
+
+<!-- Provide the basic links for the dataset. -->
+
+- **Repository:** https://github.com/SceneFun3D/scenefun3d
+  
+- **Paper:** Delitzas et al. "SceneFun3D: Fine-Grained Functionality and
+  Affordance Understanding in 3D Scenes." CVPR 2024 (Oral).
+
+- **Demo:** https://scenefun3d.github.io
+
+## Uses
+
+### Direct Use
+
+- Functional interactive element detection / segmentation in 3D point clouds.
+
+- Affordance grounding (predicting the affordance class of interactive parts).
+
+- Task-driven affordance grounding: localizing the 3D element that satisfies a
+  natural-language instruction ("open the drawer next to the sink").
+
+- Motion estimation for articulated/interactive parts (axis, direction, type).
+
+- Robotics and embodied-AI research on manipulation target selection.
+
+
+## Dataset Structure
+
+<!-- This section provides a description of the dataset fields and structure. -->
+
+This is a **grouped dataset** (`media_type = "group"`) where the group is one
+**scene** (`visit_id`). Each group has:
+
+- `laser_scan` (`3d`/FO3D) - the scene's Faro point cloud (RGB-shaded) carrying the
+  3D `functional_elements`, `objects_3d`, and `tasks` (one per scene).
+
+- `ipad_1`, `ipad_2`, ... (`video`) - one slice per iPad recording of the scene
+  (high-res RGB, 1920x1440, ~10 FPS, re-encoded to H.264 MP4), with per-frame
+  depth, pose, intrinsics, and the 3D elements/objects projected into the frame.
+  Scenes have ~2-3 recordings; positional slices are populated up to that count
+  (a 2-recording scene leaves `ipad_3` empty).
+
+The default slice is `ipad_1`. This build samples **10 scenes from each of the
+train / val / test splits** (30 scenes), and **every sample is tagged with its
+split** (`train` / `val` / `test`). Image/video/scene `metadata` is computed for
+all slices.
+
+Note: the **test split's functional annotations are withheld** by the benchmark, so
+test-split groups have the point cloud + video slices (and ARKit `objects_3d` where
+available) but no `functional_elements` / `tasks` / projected functional labels.
+
+### Sample fields (by slice)
+
+Shared:
+
+| Field | FiftyOne type | Description |
+|-------|---------------|-------------|
+| `filepath` | `StringField` | `.mp4` video (ipad_N) or `.fo3d` scene (laser_scan). |
+| `group` | `Group` | Group membership + slice name. |
+| `visit_id` | `StringField` | 6-digit scene identifier (verbatim). |
+| `tags` | `ListField(StringField)` | Source split of the sample (`train` / `val` / `test`). |
+| `metadata` | `SceneMetadata` / `VideoMetadata` | Computed media metadata (size, and frame count / dimensions for videos). |
+
+`laser_scan` slice:
+
+| Field | FiftyOne type | Description |
+|-------|---------------|-------------|
+| `functional_elements` | `Detections` | 3D functional interactive elements (one `Detection` per annotation), each linked to its 2D projections via `fo.Instance`. |
+| `objects_3d` | `Detections` | ARKit room-level object boxes (e.g. `bed`, `cabinet`, `shelf`, `tv_monitor`), aligned from the ARKit frame into the laser-scan frame; each linked to its 2D projection via `fo.Instance`. |
+| `tasks` | `ListField(StringField)` | All natural-language task descriptions for the scene. |
+
+`ipad_N` slices (one video sample per recording):
+
+| Field | FiftyOne type | Description |
+|-------|---------------|-------------|
+| `video_id` | `StringField` | 8-digit iPad sequence identifier (verbatim) of this recording. |
+| `frames[n].timestamp` | `FloatField` | Capture timestamp of the frame. |
+| `frames[n].depth` | `Heatmap` | Per-frame depth map (`map_path` to the source depth PNG in mm, `range` in mm). |
+| `frames[n].intrinsics` | `DictField` | Per-frame camera intrinsics `{width, height, fx, fy, cx, cy}`. |
+| `frames[n].camera_pose` | `ListField` | 4x4 camera-to-world pose (COLMAP, laser-scan frame), nearest-timestamp matched. |
+| `frames[n].projected_elements` | `Detections` | 2D boxes of the functional elements visible in the frame (only on frames where an element projects); `instance` links each back to its 3D box. |
+| `frames[n].projected_points` | `Keypoints` | The projected (subsampled) mask points of each visible element; same `instance` linkage. |
+| `frames[n].projected_objects` | `Detections` | 2D boxes of the ARKit room-level objects visible in the frame; `instance` links each back to its `objects_3d` box. |
+
+### `functional_elements` detection attributes
+
+Each `Detection` in `functional_elements` carries:
+
+| Attribute | Type | Description |
+|-----------|------|-------------|
+| `label` | `str` | Affordance class of the element (e.g. `rotate`, `key_press`, `tip_push`, `hook_turn`, `pinch_pull`, `plug_in`, `unplug`). |
+| `location` | `[x, y, z]` | Center of the axis-aligned 3D box, in the Faro laser-scan coordinate frame. |
+| `dimensions` | `[dx, dy, dz]` | Box size, derived from the extent of the masked points. |
+| `rotation` | `[0, 0, 0]` | Axis-aligned boxes (no orientation estimated from the mask). |
+| `annot_id` | `str` | Source annotation UUID. |
+| `num_points` | `int` | Number of laser-scan points in the element's index mask. |
+| `descriptions` | `list[str]` | Task instructions that reference this element. |
+| `motion_type` | `str` | `trans` (translation) or `rot` (rotation). |
+| `motion_dir` | `[x, y, z]` | Motion direction vector. |
+| `motion_origin` | `[x, y, z]` | Motion origin point (laser-scan coordinate of `motion_origin_idx`). |
+| `motion_viz_orient` | `str` | `inwards` / `outwards` orientation hint for visualizing the motion. |
+
+The `label` is one of the 9 Gibsonian-inspired affordance categories (paper Tab. 1):
+
+- `rotate` - adjusted by a rotary switch/knob (e.g. thermostat)
+- `key_press` - surfaces of keys that can be pressed (e.g. remote, keyboard)
+- `tip_push` - triggered by the tip of a finger (e.g. light switch)
+- `hook_pull` - pulled by hooking up fingers (e.g. fridge handle)
+- `pinch_pull` - pulled with a pinch movement (e.g. drawer knob)
+- `hook_turn` - turned by hooking up fingers (e.g. door handle)
+- `foot_push` - pushed by foot (e.g. trash-can pedal)
+- `plug_in` - electrical power sources
+- `unplug` - removing a plug from a socket
+
+(The source also has an `exclude` category for elements whose geometry is poorly
+captured, e.g. reflective materials; it is a don't-care mask, not an affordance,
+and is dropped here.)
+
+
+### What is not ingested
+
+- **Low-res iPad stream** (`lowres_wide` / `lowres_depth`, 256x192 @ 60 FPS) is not
+  imported; the hires stream is used as the single RGB video slice.
+- **Remaining ARKit-legacy assets** (`arkit_mesh` reconstruction, `vga_wide`,
+  `ultrawide` camera streams) are available from the source but not imported here.
+  (The ARKit `3dod_annotation` objects and the Faro<->ARKit `transform` *are* now
+  ingested - see `objects_3d`.)
+
+## Dataset Creation
+
+### Curation Rationale
+
+
+Most 3D scene datasets label whole objects or object parts, which is only an
+intermediate step toward agents that must actually interact with the functional
+elements (knobs, handles, buttons) to accomplish tasks. Commodity RGB-D
+reconstructions (ScanNet, Matterport) often fail to capture these small details,
+so SceneFun3D leverages high-resolution Faro laser scans. It is also the first
+dataset to link **Gibsonian** affordances (what an element affords, e.g. "press")
+with **telic** affordances (the element's purpose in scene context, e.g. "turn on
+the ceiling light") via natural-language task descriptions, plus motion parameters
+describing how to interact.
+
+### Source Data
+
+#### Data Collection and Processing
+
+
+Scenes are built on ARKitScenes captures. For each scene, multiple Faro Focus S70 laser scans (four on average) are combined under a common coordinate frame and
+downsampled with a 5mm voxel size to preserve small functional parts while remaining tractable; extraneous points from transparent surfaces (e.g. windows)
+are removed with DBSCAN and flagged by a binary crop mask. Each scene is also accompanied by iPad Pro (2020) video sequences (three on average) with RGB,
+on-device LiDAR depth, and camera trajectory. Because the iPad data and the laser scan are in different coordinate frames, the authors register them (proxy high-resolution RGB-D reconstruction + Predator + multi-scale ICP) and provide
+per-frame camera poses via rigid-body motion interpolation in SO(3) x R^3. Each scene's hires RGB-D recordings, poses, and intrinsics are ingested as the `ipad_N`
+video slices of its group.
+
+The dataset's official splits are 545 train / 80 val / 85 test scenes (710 total; ARKitScenes' validation set is used as the test set since its test set is private).
+This FiftyOne build samples 10 scenes from each split as listed in the toolkit's benchmark scene lists.
+
+#### Who are the source data producers?
+
+
+The underlying RGB-D captures and Faro laser scans come from ARKitScenes (Apple), recorded with a 2020 iPad Pro and a Faro Focus S70 laser scanner. The functional,
+motion, and language annotations were produced by the SceneFun3D authors and their annotation team.
+
+### Annotations
+
+<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
+
+#### Annotation process
+
+<!-- This section describes the annotation process. -->
+
+Annotations were collected with a custom lightweight web-based tool that supports point-accurate selection on dense high-resolution point clouds (accelerated by a
+Bounding Volume Hierarchy ray-caster, no GPU required), with the scene videos available to annotators for reference. For each functional interactive element,
+annotators (1) select a Gibsonian affordance label, (2) annotate the instance mask at single-point accuracy, (3) select the motion type (translational or rotational)
+with a motion-axis origin point and direction vector, and (4) provide free-form natural-language task descriptions that uniquely involve that element. Collected
+descriptions are additionally rephrased for diversity using OpenAI's `gpt-3.5-turbo-instruct` and verified. Elements whose geometry (or whose parent
+object) is poorly captured (e.g. reflective materials) are labeled `exclude` and omitted from the benchmark evaluation.
+
+#### Who are the annotators?
+
+<!-- This section describes the people or systems who created the annotations. -->
+
+Human annotators organized by the SceneFun3D authors, using the custom web-based annotation tool. Task-description rephrasings are machine-generated
+(`gpt-3.5-turbo-instruct`) and human-verified.
+
+## Citation
+
+**BibTeX:**
+
+```bibtex
+@inproceedings{delitzas2024scenefun3d,
+  title={{SceneFun3D: Fine-Grained Functionality and Affordance Understanding in 3D Scenes}},
+  author={Delitzas, Alexandros and Takmaz, Ayca and Tombari, Federico and Sumner, Robert and Pollefeys, Marc and Engelmann, Francis},
+  booktitle={IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+  year={2024}
+}
+```
+
+**APA:**
+
+Delitzas, A., Takmaz, A., Tombari, F., Sumner, R., Pollefeys, M., & Engelmann, F.
+(2024). SceneFun3D: Fine-Grained Functionality and Affordance Understanding in 3D
+Scenes. In *IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)*.
+
+## More Information
+
+Built on ARKitScenes (https://github.com/apple/ARKitScenes). Toolkit and
+documentation: https://scenefun3d.github.io. This FiftyOne build downloads, per
+scene, the visit-level assets (laser scan, crop mask, annotations, descriptions,
+motions) and, per recording, the hires RGB / depth / intrinsics / poses from the
+SceneFun3D release mirror plus the ARKit `3dod_annotation` and Faro<->ARKit
+`transform` (for `objects_3d`).

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