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📊 Evaluation

To reproduce the results reported in our paper, please follow the steps below.

0. Data Preparation

Step 1: General Evaluation Datasets Download the main evaluation datasets and unzip them into the project directory.

📥 Download Evaluation Datasets

Step 2: 2D Tracking Datasets (TAP-Vid) For 2D tracking benchmarks, please refer to the DeepMind TAP-Vid repository for instructions on downloading and processing the data (kinetics, rgb_stacking, robotap).

Directory Structure Ensure your directory structure matches the following layout after extraction and processing:

Track4World/
└── evaluation/
    ├── 2d_track/                # 2D Tracking (TAP-Vid)
    │   ├── robotap/
    │   ├── tapvid_kinetics/
    │   └── tapvid_rgb_stacking/
    ├── flow/                    # Optical and Scene Flow 
    │   ├── blinkvision/
    │   ├── kitti/
    │   ├── kubric_long/
    │   └── kubric_short/
    ├── point_cloud/             # Point Cloud and Video Depth
    │   ├── Bonn/
    │   ├── GMUKitchens/
    │   ├── KITTI/
    │   ├── Kubric-3D/
    │   ├── Monkaa/
    │   ├── Scannet/
    │   ├── Sintel/
    │   └── Tum/
    └── track/                   # 3D Tracking
        ├── adt_mini/
        ├── ds_mini/
        ├── po_mini/
        └── pstudio_mini/

1. Optical and Scene Flow Estimation

Run the following commands to evaluate flow estimation on different datasets:

BlinkVision & KITTI:

python evaluation/flow/eval.py --dataset kitti
python evaluation/flow/eval.py --dataset blinkvision

Kubric: Evaluate on different sequence lengths:

python evaluation/flow/eval.py --dataset kubric_short
python evaluation/flow/eval.py --dataset kubric_long

2. Point Cloud and Video Depth Estimation

We provide separate scripts for evaluating point cloud reconstruction and video depth estimation.

Point Cloud Evaluation

python evaluation/point_cloud/eval_pointcloud.py \
  --output evaluation/point_cloud/output/point/Sintel \
  --num_tokens 1200 \
  --gt-dataset-type Sintel
  • Supported Datasets (--gt-dataset-type): Tum, Sintel, Scannet, Monkaa, Kubric-3D, KITTI, GMUKitchens

Video Depth Evaluation

python evaluation/point_cloud/eval_videodepth.py \
  --output evaluation/point_cloud/output/depth/Sintel \
  --num_tokens 1200 \
  --gt-dataset-type Sintel
  • Supported Datasets (--gt-dataset-type): Bonn, Sintel, Scannet, Monkaa, Kubric-3D, KITTI, GMUKitchens

3. 3D Tracking Estimation

Evaluate 3D tracking performance by specifying the dataset and the number of frames.

python evaluation/track/eval.py \
  --dataset adt \
  --num_frames 16 \
  --world_eval

Arguments:

  • --dataset: Choose from ['adt', 'ds', 'po', 'pstudio']
  • --num_frames: Choose from [16, 50]
  • --world_eval: Evaluate results in the world coordinate system

4. 2D Tracking Estimation

Evaluate 2D tracking performance on TAP-Vid benchmarks (Kinetics, RGB-Stacking, RoboTAP).

python evaluation/2d_track/eval.py