Papers › iDisc: Internal Discretization for Monocular Depth Estimation

iDisc: Internal Discretization for Monocular Depth Estimation

13 Apr 2023CVPR 2023 1arXiv:2304.06334archive 2025-07-28

Luigi Piccinelli, Christos Sakaridis, Fisher Yu

Monocular depth estimation is fundamental for 3D scene understanding and downstream applications. However, even under the supervised setup, it is still challenging and ill-posed due to the lack of full geometric constraints. Although a scene can consist of millions of pixels, there are fewer high-level patterns. We propose iDisc to learn those patterns with internal discretized representations. The method implicitly partitions the scene into a set of high-level patterns. In particular, our new module, Internal Discretization (ID), implements a continuous-discrete-continuous bottleneck to learn those concepts without supervision. In contrast to state-of-the-art methods, the proposed model does not enforce any explicit constraints or priors on the depth output. The whole network with the ID module can be trained end-to-end, thanks to the bottleneck module based on attention. Our method sets the new state of the art with significant improvements on NYU-Depth v2 and KITTI, outperforming all published methods on the official KITTI benchmark. iDisc can also achieve state-of-the-art results on surface normal estimation. Further, we explore the model generalization capability via zero-shot testing. We observe the compelling need to promote diversification in the outdoor scenario. Hence, we introduce splits of two autonomous driving datasets, DDAD and Argoverse. Code is available at http://vis.xyz/pub/idisc .

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SysCV/idisc officialmentioned on GitHubpytorch report
lpiccinelli-eth/unidepth mentioned on GitHubpytorch report

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AttentionLayer SysCV/idisc/idisc/models/id_module.py official repository ran licence not identified · pointer only · c2e0047d60b9f291 · report
LayerNorm SysCV/idisc/idisc/models/id_module.py official repository ran fingerprinted licence not identified · pointer only · 9fd176b1d81fef0d · report
PositionEmbeddingSine SysCV/idisc/idisc/models/id_module.py official repository ran · metamorphic tier: invariant fingerprinted licence not identified · pointer only · 31f137df1d61458e · report
_get_activation_cls SysCV/idisc/idisc/models/id_module.py official repository ran · our draft was wrong licence not identified · pointer only · e220cdd4017ddf4d · report
get_norm SysCV/idisc/idisc/models/id_module.py official repository ran · our draft was wrong licence not identified · pointer only · ab2364408642790f · report
ISDHead SysCV/idisc/idisc/models/id_module.py official repository unverified licence not identified · pointer only · 6aba889a320ff6eb · report

Tasks

Autonomous DrivingDepth EstimationMonocular Depth EstimationScene UnderstandingSurface Normal EstimationSurface Normals Estimation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Monocular Depth Estimation KITTI Eigen split iDisc Delta < 1.25 0.977 #21 of 79 Archive leaderboard report
Monocular Depth Estimation KITTI Eigen split iDisc Delta < 1.25^2 0.997 #21 of 79 Archive leaderboard report
Monocular Depth Estimation KITTI Eigen split iDisc Delta < 1.25^3 0.999 #21 of 79 Archive leaderboard report
Monocular Depth Estimation KITTI Eigen split iDisc RMSE 2.067 #21 of 79 Archive leaderboard report
Monocular Depth Estimation KITTI Eigen split iDisc RMSE log 0.077 #21 of 79 Archive leaderboard report
Monocular Depth Estimation KITTI Eigen split iDisc Sq Rel 0.145 #21 of 79 Archive leaderboard report
Monocular Depth Estimation KITTI Eigen split iDisc absolute relative error 0.050 #21 of 79 Archive leaderboard report
Monocular Depth Estimation NYU-Depth V2 iDisc Delta < 1.25^2 0.993 #32 of 85 Archive leaderboard report
Monocular Depth Estimation NYU-Depth V2 iDisc Delta < 1.25^3 0.999 #32 of 85 Archive leaderboard report
Monocular Depth Estimation NYU-Depth V2 iDisc absolute relative error 0.086 #32 of 85 Archive leaderboard report
Surface Normals Estimation NYU Depth v2 iDisc % < 11.25 63.8 #3 of 6 Archive leaderboard report
Surface Normals Estimation NYU Depth v2 iDisc % < 22.5 79.8 #3 of 6 Archive leaderboard report
Surface Normals Estimation NYU Depth v2 iDisc % < 30 85.6 #3 of 6 Archive leaderboard report
Surface Normals Estimation NYU Depth v2 iDisc Mean Angle Error 14.6 #3 of 6 Archive leaderboard report
Surface Normals Estimation NYU Depth v2 iDisc RMSE 22.8 #3 of 6 Archive leaderboard report

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