Papers › LocalBins: Improving Depth Estimation by Learning Local Distributions
LocalBins: Improving Depth Estimation by Learning Local Distributions
Shariq Farooq Bhat, Ibraheem Alhashim, Peter Wonka
We propose a novel architecture for depth estimation from a single image. The architecture itself is based on the popular encoder-decoder architecture that is frequently used as a starting point for all dense regression tasks. We build on AdaBins which estimates a global distribution of depth values for the input image and evolve the architecture in two ways. First, instead of predicting global depth distributions, we predict depth distributions of local neighborhoods at every pixel. Second, instead of predicting depth distributions only towards the end of the decoder, we involve all layers of the decoder. We call this new architecture LocalBins. Our results demonstrate a clear improvement over the state-of-the-art in all metrics on the NYU-Depth V2 dataset. Code and pretrained models will be made publicly available.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Monocular Depth Estimation | NYU-Depth V2 | LocalBins | Delta < 1.25 | 0.91 | #47 of 85 | Archive leaderboard | report |
| Monocular Depth Estimation | NYU-Depth V2 | LocalBins | Delta < 1.25^2 | 0.986 | #47 of 85 | Archive leaderboard | report |
| Monocular Depth Estimation | NYU-Depth V2 | LocalBins | Delta < 1.25^3 | 0.997 | #47 of 85 | Archive leaderboard | report |
| Monocular Depth Estimation | NYU-Depth V2 | LocalBins | RMSE | 0.351 | #47 of 85 | Archive leaderboard | report |
| Monocular Depth Estimation | NYU-Depth V2 | LocalBins | absolute relative error | 0.098 | #47 of 85 | Archive leaderboard | report |
| Monocular Depth Estimation | NYU-Depth V2 | LocalBins | log 10 | 0.042 | #47 of 85 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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