Voxel51/spatial_lm_dataset

Dataset Card for Spatial LM

This is a FiftyOne 3D dataset with 19,992 samples representing indoor room point clouds with structured 3D layout and object annotations from the SpatialLM benchmark.

Each sample is an .fo3d scene containing a coloured point cloud with overlaid 3D bounding box annotations for walls, doors, windows, and furniture/objects — all browsable and queryable in the FiftyOne App.

Installation

If you haven't already, install FiftyOne:

pip install -U fiftyone

Usage

import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub

# Load the dataset
dataset = load_from_hub("Voxel51/spatial_lm_dataset")

# Launch the App
session = fo.launch_app(dataset)

Viewer Tips

• The point cloud renders at full RGB colour by default.
• Toggle walls / doors / windows / objects in the sidebar to overlay coloured 3D bounding boxes on the point cloud.
• Filter by room_type, split, or sample_type for targeted exploration.
• The viewer uses a Z-up coordinate convention (floor at Z = 0, height along +Z).

Dataset Details

Dataset Description

SpatialLM is a large-scale synthetic indoor scene dataset created by Manycore Tech Inc. and researchers at HKUST. It contains point clouds from 12,328 diverse indoor scenes comprising 54,778 rooms, each paired with ground-truth 3D annotations for architectural layout (walls, doors, windows) and oriented object bounding boxes across 59 semantic categories. The scenes are sourced from professional interior designs on the Kujiale online platform and rendered with a production-grade rendering engine.

This FiftyOne version represents 19,992 room-level samples as .fo3d 3D scenes. Each sample pairs a PLY point cloud (rendered as coloured points in world-space coordinates) with fo.Detections fields for walls, doors, windows, and objects — making all annotations browsable, filterable, and queryable directly in the FiftyOne App's 3D viewer.

• Curated by: Yongsen Mao, Junhao Zhong, Chuan Fang, Jia Zheng, Rui Tang, Hao Zhu, Ping Tan, Zihan Zhou — Manycore Tech Inc. and Hong Kong University of Science and Technology
• FiftyOne integration by: Harpreet Sahota
• Language(s): en (annotations are English category labels)
• License: CC-BY-NC-4.0

Dataset Sources

• Project page: manycore-research.github.io/SpatialLM
• Paper: arXiv:2506.07491 (NeurIPS 2025)
• Code: github.com/manycore-research/SpatialLM
• Original dataset: huggingface.co/datasets/manycore-research/SpatialLM-Dataset
• Model weights: huggingface.co/manycore-research/SpatialLM1.1-Qwen-0.5B

FiftyOne Dataset Structure

Scene Format

Each sample's filepath points to an .fo3d scene file containing a single fo.PlyMesh node with is_point_cloud=True. The PLY files carry per-vertex RGB colours which FiftyOne renders automatically. center_geometry=False preserves the original world-space coordinates so annotations stay spatially aligned with the point cloud.

The scene uses a dark background (#1a1a2e) and a fo.PerspectiveCamera with up="Z".

Sample Fields

Field	Type	Description
filepath	str	Path to the .fo3d scene file
sample_id	str	Unique identifier following {scene_id}_{room_id}_{sample} convention (e.g., scene_001523_00_2)
scene_id	str	Identifier for the multi-room apartment scene
room_type	str	Functional room type (e.g., bedroom, living_room, kitchen, bathroom, balcony)
sample_type	int	Point cloud sampling configuration: 0 = 8 panoramic views (most complete), 1 = 8 perspective views (most sparse), 2 = 16 perspective views, 3 = 24 perspective views
split	str	Dataset partition: train, val, test, or reserved
walls	fo.Detections	Wall annotations as 3D bounding boxes
doors	fo.Detections	Door annotations as 3D bounding boxes
windows	fo.Detections	Window annotations as 3D bounding boxes
objects	fo.Detections	Furniture/object annotations as oriented 3D bounding boxes

3D Detection Fields

All annotation fields use fo.Detection with 3D attributes:

Attribute	Description
label	Semantic category (e.g., wall, door, window, sofa, bed)
location	[x, y, z] centre position in world-space coordinates
dimensions	[sx, sy, sz] bounding box extents in metres
rotation	[rx, ry, rz] Euler rotation; walls and openings use yaw (Z-rotation) derived from wall endpoints

Walls have tags containing the wall ID (e.g., wall_0). Doors and windows have tags containing both the opening ID and the parent wall ID they are attached to (e.g., ["door_0", "wall_16"]).

A minimum thickness of 0.05 m is enforced on walls and openings so they remain visible in the 3D viewer.

Coordinate System

The dataset uses a Z-up convention: the floor sits at Z = 0, height extends along +Z. Coordinates are quantized into 1,280 bins at 2.5 cm resolution in the original SpatialLM format and stored as continuous float values in FiftyOne.

Dataset Splits

Split	Scenes	Point Cloud Samples
Train	11,328	~199,286
Val	500	500
Test	500	500
Reserved	—	—

Multiple point cloud samples of the same room (varying observation completeness) exist in the training set. Val/test splits use a single random sample per room.

Object Categories

The dataset annotates 59 object categories (excluding walls, doors, and windows) organised by function:

Super Category	Categories
Seating	sofa, chair, dining_chair, bar_chair, stool
Bedding	bed, pillow
Cabinetry	wardrobe, nightstand, tv_cabinet, wine_cabinet, bathroom_cabinet, shoe_cabinet, entrance_cabinet, decorative_cabinet, washing_cabinet, wall_cabinet, sideboard, cupboard
Tables	coffee_table, dining_table, side_table, dressing_table, desk
Kitchen	integrated_stove, gas_stove, range_hood, microwave_oven, sink, stove, refrigerator
Bathroom	hand_sink, shower, shower_room, toilet, tub
Lighting	illumination, chandelier, floor_standing_lamp
Decoration	wall_decoration, painting, curtain, carpet, plants, potted_bonsai
Electronics	tv, computer, air_conditioner, washing_machine
Other	clothes_rack, mirror, bookcase, cushion, bar, screen, combination_sofa, dining_table_combination, leisure_table_and_chair_combination, multifunctional_combination_bed

Small objects with all side lengths below 15 cm are filtered out. The dataset contains 412,932 annotated object instances from 35,426 unique CAD models.

Dataset Creation

Curation Rationale

SpatialLM was created to address the lack of large-scale, high-quality datasets that provide both architectural layout annotations (walls, doors, windows) and 3D object bounding boxes for indoor scenes. Existing real-world datasets like ScanNet and ScanCAD are limited in scale (hundreds to low thousands of scenes) and typically provide only object annotations. Existing synthetic alternatives either lack visual realism (ProcTHOR, ASE — procedurally generated) or are too small (Hypersim: 461 scenes, HSSD: 211 scenes). SpatialLM fills this gap by leveraging professionally designed interiors at scale.

Source Data

Data Collection and Processing

The 3D scenes originate from a large repository of interior designs on Kujiale, an online interior design platform. Most designs were created by professional designers for real-world production use. The authors parsed each 3D house model into individual rooms and applied filtering rules, yielding 12,328 distinct scenes with 54,778 rooms.

For each scene, photo-realistic RGBD images were rendered using a production-grade rendering engine. Camera trajectories traversing each room were simulated, with images captured at 0.5 m intervals. Point clouds were then reconstructed from these RGBD renders. Each room has up to 4 point cloud sampling configurations varying in observation completeness (8 panoramic views through 24 perspective views).

Who are the source data producers?

Professional interior designers using the Kujiale platform. The scenes reflect real-world Chinese residential interior design conventions and standards.

Annotations

Annotation process

Annotations are derived directly from the source 3D scene geometry — no manual annotation was required. Walls, doors, and windows are extracted from the architectural model. Object bounding boxes (oriented 3D boxes with semantic categories) are computed from the placed 3D assets in the design. Objects are annotated across 59 categories, with small objects

Citation

@inproceedings{SpatialLM,
  title     = {SpatialLM: Training Large Language Models for Structured Indoor Modeling},
  author    = {Mao, Yongsen and Zhong, Junhao and Fang, Chuan and Zheng, Jia and Tang, Rui and Zhu, Hao and Tan, Ping and Zhou, Zihan},
  booktitle = {Advances in Neural Information Processing Systems},
  year      = {2025}
}