Clearpose Eval

---
name: clearpose-eval
description: Evaluates perception models' ability to estimate 6 DoF poses and complete depth maps for transparent and translucent objects. It specifically probes robustness to challenging real-world conditions such as heavy occlusion, cluttered backgrounds, varying lighting, and objects filled with liquid. Use when the user wants to benchmark on ClearPose, or asks about evaluating this task. Reports 6 DoF poses.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2203.03890
  bibtex_key: chen2022clearpose
  confidence: medium
---

# clearpose-eval

> ClearPose: Large-scale Transparent Object Dataset and Benchmark — Chen et al. (2022) (arXiv:2203.03890, 2022)

## What this evaluates

Evaluates perception models' ability to estimate 6 DoF poses and complete depth maps for transparent and translucent objects. It specifically probes robustness to challenging real-world conditions such as heavy occlusion, cluttered backgrounds, varying lighting, and objects filled with liquid.

## Datasets

- **ClearPose** — total 354481; splits: train (-1), test (-1); repo https://github.com/opipari/ClearPose

## Metrics

- `6 DoF poses` **(primary)** — range: other
  - Not explicitly defined in the provided text; typically measures translation distance and rotation angle between predicted and ground truth object poses.

## Input / output format

**Input**: RGB-D frames (640x480) capturing transparent objects in various scenes.

**Output**: 6-DoF object poses, segmentation masks, surface normals, and fixed ground truth depth images.

## Scoring recipe

```python
def compute_metrics(pred_poses, gt_poses, pred_depth, gt_depth):
    t_err = np.linalg.norm(pred_poses[:, :3, 3] - gt_poses[:, :3, 3])
    r_err = np.arccos(np.clip((np.trace(pred_poses[:, :3, :3].T @ gt_poses[:, :3, :3]) - 1) / 2, -1, 1))
    depth_rmse = np.sqrt(np.mean((pred_depth - gt_depth) ** 2))
    return {'pose_error': t_err + r_err, 'depth_rmse': depth_rmse}
```

## Common pitfalls

- Transparent objects yield broken or inconsistent depth measurements, requiring specialized handling beyond standard RGB-D pipelines.
- The test set is divided into 6 specific challenging scenarios (e.g., heavy occlusion, liquid-filled objects, non-planar surfaces) that are strictly held out from training to evaluate generalization.

## Evidence (verbatim from paper)

> In total there are 354,481 RGB-D frames captured in 51 scenes, with 5,052,429 object instance annotations with 6 DoF poses, segmentation masks, surface normals, and ground truth depth images. For the testing set, as the appearance of transparent objects depends on their context within a scene, we consider 6 different test cases and collect corresponding scenes as follows: (1) different backgrounds: novel backgrounds that never appeared in the training scenes with each object subset. (2) heavy occlusions: cluttered scenes each with about 25 objects that form multiple layers of occlusion when viewed from the table’s side. (3) translucent/transparent covers: scenes with all transparent objects placed inside a translucent box. (4) together with opaque objects: transparent objects placed together with opaque YCB and HOPE objects, which did not appear in the training set. (5) filled with liquid: scenes with transparent objects filled with different colored liquid. (6) non-planar configuration: scenes with objects placed onto different surfaces with multiple heights.

## Citation

```bibtex
@misc{chen2022clearpose,
  title={ClearPose: Large-scale Transparent Object Dataset and Benchmark},
  author={Chen et al. (2022)},
  year={2022},
  note={arXiv:2203.03890}
}
```

- arXiv: 2203.03890