Datasets:

KangLiao
/

DL3DV-Depth-DA3-Aligned

license: apache-2.0
task_categories:
  - depth-estimation
tags:
  - depth
  - depth-anything-3
  - dl3dv
  - metric-depth
size_categories:
  - 1M<n<10M

DL3DV-Depth-DA3-Aligned

Per-frame depth annotations for the DL3DV dataset, produced by Depth-Anything-3 (DA3) and then aligned to each scene's sparse depth from the original DL3DV reconstruction.

Directory structure

The archive mirrors the source DL3DV / DL3DV-ALL-960P layout — one .zip per scene, grouped into bucket folders 1K–7K (<bucket>/<scene_hash>.zip). The scene hashes match DL3DV-ALL-960P and KangLiao/DL3DV-Absolute-Camera, so depth pairs 1:1 with the source frames / camera annotations.

Each <scene_hash>.zip unpacks to:

dense/
└── depth_da3/
    ├── frame_00001.npy
    ├── frame_00002.npy
    ├── frame_00003.npy
    └── ...

Each frame_NNNNN.npy is a float32 depth map — np.load(...) returns an array of shape (H, W) (e.g. (536, 954)), one per source frame, indices matching the DL3DV frames.

How the depth was produced

Predicted with Depth-Anything-3 (DA3).
Aligned to the sparse depth of the original DL3DV dataset (per-scene alignment against the sparse reconstruction), so each scene's DA3 depth is brought into a consistent, scale-aligned space.

Usage

import numpy as np
depth = np.load("dense/depth_da3/frame_00001.npy")  # (H, W) float32

Notes

~6,377 scenes; each .npy frame ≈ 2 MB (float32), stored losslessly.
Companion camera annotations: KangLiao/DL3DV-Absolute-Camera.