C&L - 20/04/26
The past experiments were about again trying to have a purely diffusion based approach - of the marigold adjacent diffusion architecture - converge towards a reasonable metric depth estimator.
Week 17, 2026 · Apr 20 – Apr 26
Week 15, 2026 · Apr 6 – Apr 12
Late March: Adding Uncertainty to Mari & Evaluating Against Marigold
The second half of March was about one core question: can we extend Mari from predicting just RGB & depth to also predicting visibility (uncertainty), and does that improve depth estimation in fog? This post walks through how the experiments progressed — from a major code refactor, through training the new uncertainty head, to comparing results against baseline Marigold.
Week 14, 2026 · Mar 30 – Apr 5
Comparing Relative Depth
This week was mostly about evaluating the current model, comparing it naiively with Marigold - always scale&shifting the relative depth estimation output of both models to be comparable with the metric ground truth.
Week 9, 2026 · Feb 23 – Mar 1
24.02 Experiments: DiT's vs U-Net
Last two weeks were spent with lots of experiments on the RDS dataset and MERGE architecture.Status quo was:- Dehazing via SD2.1 works well- Novelty from MERGE: Use Marigold adjacent architecture with dual-converters ("prediction heads") for image generation and depth maps.- Early MERGE experiments worked well on VKITTI2 (352 x 1216 RGB)- Extend tests to real-drive-sim
Week 7, 2026 · Feb 9 – Feb 15
Clear and Look - Using Diffusion Models for depth prediction in bad wheather
As a placeholder, we name the model we will train as:
- : Joint dehazing (RGB) and Depth-Prediction (2D Depth Map)
- : Only dehaze (RGB)
We will use UniDepthV2 as a basline to compare:
- UniDepthV2(Foggy Image) <-> UniDepthV2()