the-masses/ReplicaOcc

Replica_OCC Benchmark

Replica_OCC is a Replica-based occupancy benchmark constructed in the data organization style of EmbodiedOcc-ScanNet and OccScanNet. It provides RGB-D sequences and scene-level occupancy ground truth for evaluating embodied occupancy prediction systems.

Ground-truth occupancy and poses are intended for evaluation-time alignment and metric computation. They are not required for training FreeOcc and are not used for map construction.

Released Files

The released dataset package contains:

Replica_OCC/
├── README.md
├── replica_name.txt
├── prepare_preprocessed.py
├── prepare_scene_occ.py
├── vis_preprocessed.py
├── vis_scene_occ.py
└── Replica_OCC/
    ├── preprocessed/
    ├── global_occ_package/
    └── sequences/

Only the four preparation/visualization scripts above are part of the released benchmark utilities.

Dataset Layout

Replica_OCC/
├── preprocessed/
│   ├── office0.npy
│   ├── office1.npy
│   ├── office2.npy
│   ├── office3.npy
│   ├── office4.npy
│   ├── room0.npy
│   ├── room1.npy
│   └── room2.npy
├── global_occ_package/
│   ├── office0.pkl
│   ├── office1.pkl
│   ├── office2.pkl
│   ├── office3.pkl
│   ├── office4.pkl
│   ├── room0.pkl
│   ├── room1.pkl
│   └── room2.pkl
└── sequences/
    ├── cam_params.json
    ├── office0/
    │   ├── color/
    │   │   ├── 0.jpg
    │   │   └── ...
    │   ├── depth/
    │   │   ├── 0.png
    │   │   └── ...
    │   ├── pose/
    │   │   ├── 0.txt
    │   │   └── ...
    │   └── intrinsic/
    │       ├── intrinsic_color.txt
    │       ├── intrinsic_depth.txt
    │       ├── extrinsic_color.txt
    │       └── extrinsic_depth.txt
    ├── office1/
    ├── office2/
    ├── office3/
    ├── office4/
    ├── room0/
    ├── room1/
    └── room2/

For FreeOcc evaluation, use:

DATA_ROOT=/path/to/Replica_OCC/sequences
SCENE_OCC_ROOT=/path/to/Replica_OCC

Coordinate System

The occupancy ground truth is built in the original Replica world coordinate system.

prepare_preprocessed.py back-projects depth pixels using Replica camera poses and intrinsics. The resulting sparse semantic voxel points are stored in Replica world coordinates. prepare_scene_occ.py then builds a dense occupancy grid from those points and saves voxel center coordinates in the same Replica world coordinate system.

Preparation Pipeline

The benchmark is created in two main stages, followed by optional visualization checks.

1. Build Sparse Semantic Voxels

prepare_preprocessed.py reads raw Replica RGB-D/semantic data and camera poses. For each scene, it back-projects depth pixels into 3D world points, assigns semantic labels from the semantic-id images, and voxelizes them by majority vote.

Input expected by the script:

Replica_SLAM/
├── cam_params.json
├── office0/
│   ├── depths/depth000000.png
│   ├── semantic_ids/semantic_id000000.png
│   └── traj.txt
└── ...

Example command:

python prepare_preprocessed.py \
  --replica_root ./Replica_SLAM \
  --out_root ./Replica_OCC \
  --stride 4 \
  --depth_scale -1 \
  --max_depth 10.0 \
  --max_frames -1

Output:

Replica_OCC/preprocessed/<scene>.npy

Each .npy stores an array of shape (N, 7):

x, y, z, r, g, b, label

The RGB columns are placeholders; the semantic label is stored in the last column.

2. Inspect Sparse Voxels

vis_preprocessed.py visualizes the sparse semantic voxels produced by the previous step.

python vis_preprocessed.py \
  --npy ./Replica_OCC/preprocessed/office0.npy

This is mainly a sanity check for depth back-projection and semantic labels.

3. Build Scene-Level Occupancy Packages

prepare_scene_occ.py converts preprocessed/<scene>.npy into a dense scene-level occupancy package. It builds a regular voxel grid, assigns labels by nearest-neighbor lookup, and computes an observed-space mask by projecting voxels into Replica depth frames.

Example command:

python prepare_scene_occ.py \
  --replica_root ./Replica_SLAM \
  --preprocessed_dir ./Replica_OCC/preprocessed \
  --out_dir ./Replica_OCC/global_occ_package \
  --obs_stride_frame 1 \
  --obs_stride_pix 1 \
  --mask_dilate 0 \
  --obs_max_frames -1 \
  --max_depth 10.0

Output:

Replica_OCC/global_occ_package/<scene>.pkl

Each .pkl contains:

scene_name       scene id
scene_dim        dense occupancy grid dimensions
global_pts       dense voxel centers in Replica world coordinates
global_labels    voxel labels
global_mask      observed-space mask
valid_img_count  number of depth images used for mask construction
valid_img_paths  image paths used by the mask builder

Label convention:

0    known free space
>0   occupied semantic label
255  unknown / unobserved

4. Inspect Final Occupancy Packages

vis_scene_occ.py visualizes the final scene-level occupancy package.

python vis_scene_occ.py \
  --pkl ./Replica_OCC/global_occ_package/office0.pkl \
  --downsample 1

Visualization color meaning:

mask = 0              unknown region
mask = 1, label = 0   known free space
mask = 1, label > 0   occupied semantic voxels

Labels

replica_name.txt stores the semantic class names used by Replica_OCC. Evaluation and visualization scripts can use this file to map semantic label ids to readable names.

Scenes

Replica_OCC contains the following eight Replica scenes:

office0
office1
office2
office3
office4
room0
room1
room2

Notes

• preprocessed/*.npy is an intermediate representation used to reproduce global_occ_package/*.pkl.
• global_occ_package/*.pkl is the occupancy ground truth used by evaluation.
• sequences/* is the RGB-D input used by SLAM and Gaussian mapping.
• The dataset follows a ScanNet-like RGB-D folder layout so it can be used by FreeOcc's shared dataloader.