CHANGELOG.md

Changelog

All notable changes to this project will be documented in this file.

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Goodbye 2025, 2026 is near 🎉

[ApDepth-v1-2] - 2025-11-25

We present a new Loss_Function for our two stage training strats. We use MSE_Loss for stage one to learn the whole information and stage two uses our propoesd Latent_Frequence_Loss to refine edge detail

Training Framework:

Inference Framework:

[ApDepth-v1-1] - 2025-10-09

We use a pretrained model as a "teacher" to "teach" stable diffusion 2-1 to generate Depth Map.

Training Framework:

Inference Framework:

[ApDepth-v1-0] - 2025-09-23

Added

We change Marigold from Stochastic multi-step generation to Deterministic one-step perception. And achieve the same result.

Result

Method	# Training Samples		NYUv2		KITTI		ETH3D		ScanNet		DIODE		Avg. Rank
	Real	Synthetic	AbsRel↓	δ1↑	AbsRel↓	δ1↑	AbsRel↓	δ1↑	AbsRel↓	δ1↑	AbsRel↓	δ1↑
Marigold (w/o)	—*	74K	6.0	95.9	10.5	90.4	7.1	95.1	6.9	94.5	31.0	77.2	-
Marigold			5.5	96.4	9.9	91.6	6.5	96.0	6.4	95.1	30.8	77.3	-
Ours	—*	74K	5.3	96.5	10.7	89.3	6.5	95.8	6.0	96.3	30.0	77.0	-

[Marigold-depth-v1-0] - 2023-12-04

The Result is showed in table

Method	# Training Samples		NYUv2		KITTI		ETH3D		ScanNet		DIODE		Avg. Rank
	Real	Synthetic	AbsRel↓	δ1↑	AbsRel↓	δ1↑	AbsRel↓	δ1↑	AbsRel↓	δ1↑	AbsRel↓	δ1↑
DiverseDepth [56]	320K	—	11.7	87.5	19.0	70.4	22.8	69.4	10.9	88.2	37.6	63.1	7.6
MiDaS [35]	2M	—	11.1	88.5	23.6	63.0	18.4	75.2	12.1	84.6	33.2	71.5	7.3
LeReS [57]	300K	54K	9.0	91.6	14.9	78.4	17.1	77.7	9.1	91.7	27.1	76.6	5.2
Omnidata [13]	11.9M	310K	7.4	94.5	14.9	83.5	16.6	77.8	7.5	93.6	33.9	74.2	4.8
HDN [60]	300K	—	6.9	94.8	11.5	86.7	12.1	83.3	8.0	93.9	24.6	78.0	3.2
DPT [36]	1.2M	188K	9.8	90.3	10.0	90.1	7.8	94.6	8.2	93.4	18.2	75.8	3.9
Marigold (w/o)	—*	74K	6.0	95.9	10.5	90.4	7.1	95.1	6.9	94.5	31.0	77.2	2.5
Marigold			5.5	96.4	9.9	91.6	6.5	96.0	6.4	95.1	30.8	77.3	1.4

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