sunrgbd

The SUN RGB-D dataset for 3D object detection, as described in the paper SUN RGB-D: A RGB-D Scene Understanding Benchmark Suite.

First page of SUN RGB-D: A RGB-D Scene Understanding Benchmark Suite

CVPR · June 2015

Classes:

SunRGBD –

The SUN RGB-D dataset for 3D object detection, as described in the paper

Functions:

rebase_sequence –

Rebase an absolute SUN RGB-D scene path to a sequence id relative to SUNRGBD/.
decode_depth –

Decode a 16-bit SUN RGB-D depth image into metric depth.
unproject –

Unproject a metric depth map into the upright depth coordinate frame.
parse_boxes –

Parse SUN RGB-D 3D boxes into the packed detection encoding.

SunRGBD [source] ¶

SunRGBD(
    root: PathLike,
    *,
    train: bool = True,
    transform: Optional[Callable] = None,
    download: bool = False,
    force_download: bool = False,
    force_process: bool = False,
    show_progress: bool = True,
    num_workers: Optional[int] = None,
)

Bases: PointCloudDataset

The SUN RGB-D dataset for 3D object detection, as described in the paper SUN RGB-D: A RGB-D Scene Understanding Benchmark Suite.

SUN RGB-D provides 10335 RGB-D frames with amodal oriented 3D bounding boxes. This dataset reconstructs the upright point cloud from the raw depth frames following the votenet recipe ( facebookresearch/votenet) and exposes per-scene clouds with their ground-truth boxes over the 10 detection classes.

The dataset reads the depth and RGB frames directly from the 6.8 GB SUNRGBD.zip release and the metadata from SUNRGBDtoolbox.zip, without extracting either archive wholesale. Each scene is processed into a <processed_dir>/<split>/<sequence_id>/ directory holding one .npy per attribute; pos is stored as float16 and color as uint8 to keep the cache compact. Construction only enumerates the cached scene directories; each scene's arrays are read from disk on access. Cached boxes keep the on-disk \((K, 8)\) encoding (half extents, clockwise heading, trailing class column) and the per-box classes stay in class.npy; __getitem__ converts them to the emitted \((K, 7)\) box / \((K,)\) label format below, so existing caches stay valid.

Parameters:

root (PathLike) –

Root directory where the dataset is stored or will be downloaded.
train (bool, default: True ) –

If True, loads the train split; if False, loads the val split.
transform (Optional[Callable], default: None ) –

A callable that transforms the data when retrieved from the dataset.
download (bool, default: False ) –

Whether to download missing raw inputs if not present.
force_download (bool, default: False ) –

Whether to force the download of the raw data.
force_process (bool, default: False ) –

Whether to force the processing of the raw data.
show_progress (bool, default: True ) –

Whether to show a progress bar during processing.
num_workers (Optional[int], default: None ) –

Worker processes for preprocessing, or None for sequential processing.

Shape

pos: \((N, 3)\) point coordinates in the upright depth frame.
color: \((N, 3)\) RGB values in \([0, 255]\) (uint8).
box: \((K, 7)\) boxes as \([cx, cy, cz, dx, dy, dz, \text{heading}]\) with full extents and a counter-clockwise heading about \(+z\) from \(+x\).
label: \((K,)\) per-box class indices.

Example

Assuming you have downloaded SUNRGBD.zip and SUNRGBDtoolbox.zip under data/SunRGBD/raw, you can load the dataset as follows:

from torch_pointcloud.datasets import SunRGBD

dataset = SunRGBD(root="data", train=False)
sample = dataset[0]
sample["pos"].shape  # (N, 3)
sample["box"].shape  # (K, 7)

Methods:

read_split –

Read the split's sequence ids from the toolbox archive.
read_meta –

Read the per-scene metadata structs from the toolbox archive, keyed by sequence id.
process_scene –

Unproject one scene and write its packed pos / color / box / class arrays.
read_scene_cloud –

Unproject one scene's depth and RGB images into a colored point cloud.

Attributes:

release_zip_path (str) –

Path to the raw SUNRGBD.zip release archive.
toolbox_zip_path (str) –

Path to the raw SUNRGBDtoolbox.zip archive holding the splits and metadata.
class_to_idx (Dict[str, int]) –

Mapping from class name to label index.
processed_files (List[Path]) –

Sorted list of the split's processed scene directories.
name (str) –

Name of the dataset directory.
data_dir (str) –

Path to the dataset directory <root>/<name>.
raw_dir (str) –

Path to the raw download directory.
processed_dir (str) –

Path to the processed cache directory.

release_zip_path `property` ¶

release_zip_path: str

Path to the raw SUNRGBD.zip release archive.

toolbox_zip_path `property` ¶

toolbox_zip_path: str

Path to the raw SUNRGBDtoolbox.zip archive holding the splits and metadata.

class_to_idx `cached` `property` ¶

class_to_idx: Dict[str, int]

Mapping from class name to label index.

processed_files `property` ¶

processed_files: List[Path]

Sorted list of the split's processed scene directories.

name `property` ¶

name: str

Name of the dataset directory.

data_dir `property` ¶

data_dir: str

Path to the dataset directory <root>/<name>.

raw_dir `property` ¶

raw_dir: str

Path to the raw download directory.

processed_dir `property` ¶

processed_dir: str

Path to the processed cache directory.

read_split [source] ¶

read_split() -> List[str]

Read the split's sequence ids from the toolbox archive.

read_meta [source] ¶

read_meta() -> Dict[str, Any]

Read the per-scene metadata structs from the toolbox archive, keyed by sequence id.

process_scene [source] ¶

process_scene(args: Tuple[str, Any, str]) -> None

Unproject one scene and write its packed pos / color / box / class arrays.

Parameters:

args (Tuple[str, Any, str]) –

The scene's sequence id, its metadata struct, and the split directory to write into.

read_scene_cloud [source] ¶

read_scene_cloud(entry: Any) -> Tuple[ndarray, ndarray]

Unproject one scene's depth and RGB images into a colored point cloud.

Parameters:

entry (Any) –

The scene's metadata struct, holding the image paths, the intrinsics K and the upright rotation Rtilt.

Returns:

Tuple[ndarray, ndarray] –

The upright points \((N, 3)\) and their RGB colors \((N, 3)\), with the zero-depth pixels dropped.

rebase_sequence [source] ¶

rebase_sequence(path: str) -> str

Rebase an absolute SUN RGB-D scene path to a sequence id relative to SUNRGBD/.

The split lists and metadata store absolute paths such as /n/fs/sun3d/data/SUNRGBD/kv1/NYUdata/NYU0001, sometimes with a leading double slash. Members inside SUNRGBD.zip are keyed as SUNRGBD/kv1/NYUdata/NYU0001/..., so the sequence id keeps everything after the last /SUNRGBD/ segment.

Parameters:

path (str) –

Absolute scene path or a SUNRGBD/... sequence path.

Returns:

str –

The sequence id, e.g. kv1/NYUdata/NYU0001.

Examples:

>>> rebase_sequence("/n/fs/sun3d/data/SUNRGBD/kv1/NYUdata/NYU0001")
'kv1/NYUdata/NYU0001'

decode_depth [source] ¶

decode_depth(
    depth_png: ndarray,
    depth_trunc: float = DEPTH_TRUNC,
    depth_scale: float = DEPTH_SCALE,
) -> ndarray

Decode a 16-bit SUN RGB-D depth image into metric depth.

The raw PNG stores depth bit-shifted by 3. The value is recovered as (d >> 3) | (d << 13) in uint16, scaled to meters and truncated at \(8\) m.

Parameters:

depth_png (ndarray) –

Raw depth image of shape \((H, W)\) and dtype uint16.

Returns:

ndarray –

Metric depth of shape \((H, W)\) and dtype float32.

Shape

Input: \((H, W)\).
Output: \((H, W)\).

unproject [source] ¶

unproject(
    depth_m: ndarray, k: ndarray, rtilt: ndarray
) -> ndarray

Unproject a metric depth map into the upright depth coordinate frame.

Pixels are unprojected on a 1-indexed grid using the intrinsics k, reordered into the depth frame as \([x, z, -y]\), then rotated upright by rtilt. Invalid (zero-depth) pixels are kept in place here and dropped by the caller via the depth mask.

Parameters:

depth_m (ndarray) –

Metric depth of shape \((H, W)\).
k (ndarray) –

Camera intrinsics of shape \((3, 3)\).
rtilt (ndarray) –

Upright rotation of shape \((3, 3)\).

Returns:

ndarray –

Unprojected points of shape \((H \cdot W, 3)\) in row-major order, dtype float32.

Shape

Input depth_m: \((H, W)\).
Output: \((H \cdot W, 3)\).

parse_boxes [source] ¶

parse_boxes(
    gt: Any, class_to_idx: Dict[str, int]
) -> ndarray

Parse SUN RGB-D 3D boxes into the packed detection encoding.

Each kept box is \([cx, cy, cz, dx, dy, dz, \text{heading}, \text{sem\_cls}]\) where the centroid is the box center and the half-extents are the abs coeffs reordered to \([c_1, c_0, c_2]\). This matches votenet's _bbox.npy [l, w, h] mapping (\(l = \text{coeffs}[1]\) along the heading axis, \(w = \text{coeffs}[0]\), \(h = \text{coeffs}[2]\)); storing the raw coeffs order swaps \(dx \leftrightarrow dy\) and collapses oriented-box AP@0.5. The heading is \(-\text{atan2}(o_1, o_0)\) from the orientation vector, and sem_cls is the index of an exact class-name match. Objects whose class name is not one of the 10 SUN RGB-D detection classes are dropped.

Parameters:

gt (Any) –

The raw groundtruth3DBB value from the metadata struct.
class_to_idx (Dict[str, int]) –

Mapping from class name to integer index.

Returns:

ndarray –

Boxes of shape \((K, 8)\) and dtype float32 (empty \((0, 8)\) when no box is kept).

Shape

Output: \((K, 8)\).

sunrgbd

SunRGBD [source] ¶

release_zip_path property ¶

toolbox_zip_path property ¶

class_to_idx cached property ¶

processed_files property ¶

name property ¶

data_dir property ¶

raw_dir property ¶

processed_dir property ¶

read_split [source] ¶

read_meta [source] ¶

process_scene [source] ¶

read_scene_cloud [source] ¶

rebase_sequence [source] ¶

decode_depth [source] ¶

unproject [source] ¶

parse_boxes [source] ¶

release_zip_path `property` ¶

toolbox_zip_path `property` ¶

class_to_idx `cached` `property` ¶

processed_files `property` ¶

name `property` ¶

data_dir `property` ¶

raw_dir `property` ¶

processed_dir `property` ¶