The Dresden Dataset for 4D Reconstruction of Non-Rigid Abdominal Surgical Scenes
Download Dataset (DOI: 10.25532/OPARA-1033)
Data were acquired from porcine cadaver sessions using a da Vinci Xi stereo endoscope and a Zivid structured-light camera, registered via optical tracking. The dataset enables quantitative geometric evaluation of 3D reconstruction in both visible and occluded regions.
Each session contains multiple clips showing tissue manipulations with paired endoscopic video and structured-light geometry.
Requires Python 3.10
pip install -e .from d4d.loader import D4D
from visualize import visualize_clip_state
# Load dataset
d4d = D4D("/path/to/preprocessed_restructured")
# Iterate: Dataset → Specimen → Session → Clip
for specimen in d4d:
for session in specimen:
for clip in session:
print(f"{clip.name}: {len(clip.left_img_paths)} images, {clip.duration:.1f}s")
# Access specific clip
specimen = next(iter(d4d))
session = next(iter(specimen))
clip = next(iter(session))
# Clip properties
clip.left_img_paths # List of left image paths
clip.right_img_paths # List of right image paths
clip.stereo_depth_paths # List of depth map paths
clip.pointclouds # Dict with 'start'/'end' Zivid PLY paths
clip.endoscope_params # Endoscope camera parameters
clip.zivid_params # Zivid camera parameters
clip.poses # Dict with 'start'/'end' curated camera poses
# Visualize with curated poses
if clip.pointclouds.get('start') and clip.left_img_paths and clip.stereo_depth_paths:
visualize_clip_state(
clip.pointclouds['start'],
clip.left_img_paths[0],
clip.stereo_depth_paths[0],
clip.endoscope_params,
clip.poses['start']
)