BetterDepth: Plug-and-Play Diffusion Refiner for Zero-Shot Monocular
  Depth Estimation
By training over large-scale datasets, zero-shot monocular depth estimation
(MDE) methods show robust performance in the wild but often suffer from
insufficiently precise details. Although recent diffusion-based MDE approaches
exhibit appealing detail extraction ability, they still struggle in
geometrically challenging scenes due to the difficulty of gaining robust
geometric priors from diverse datasets. To leverage the complementary merits of
both worlds, we propose BetterDepth to efficiently achieve geometrically
correct affine-invariant MDE performance while capturing fine-grained details.
Specifically, BetterDepth is a conditional diffusion-based refiner that takes
the prediction from pre-trained MDE models as depth conditioning, in which the
global depth context is well-captured, and iteratively refines details based on
the input image. For the training of such a refiner, we propose global
pre-alignment and local patch masking methods to ensure the faithfulness of
BetterDepth to depth conditioning while learning to capture fine-grained scene
details. By efficient training on small-scale synthetic datasets, BetterDepth
achieves state-of-the-art zero-shot MDE performance on diverse public datasets
and in-the-wild scenes. Moreover, BetterDepth can improve the performance of
other MDE models in a plug-and-play manner without additional re-training.
66a355b0e88715bf1bb3094e
Xiang Zhang
claimed_verified
2024-07-26T10:26:32.542Z
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/avatars/3bba11ac7659fce61aeaedf40e2057a8.svg
Xiang Zhang
user
XiangZ
66a355b0e88715bf1bb3094f
Bingxin Ke
admin_assigned
2024-07-26T08:39:19.926Z
63d90391da4f72339244c2a8
/avatars/eb0e0259c391d59739c1a205c36bb539.svg
Bingxin Ke
user
Bingxin
66a355b0e88715bf1bb30950
Hayko Riemenschneider
admin_assigned
2024-07-26T08:39:27.141Z
6332ab213c1494be1137bd58
/avatars/f65dac80d7f5148c46781055ddbdae51.svg
hayko riemenschneider
user
chrysmun
66a355b0e88715bf1bb30951
Nando Metzger
admin_assigned
2024-07-26T08:39:32.523Z
63a5785a8fb23d08bb2d0291
/avatars/758b06dae06e9eee6fced10ce682aef1.svg
Nando Metzger
user
nandometzger
66a355b0e88715bf1bb30952
Anton Obukhov
claimed_verified
2024-07-26T08:05:55.086Z
62f93abbc4817cfc0756b6f8
https://cdn-avatars.huggingface.co/v1/production/uploads/62f93abbc4817cfc0756b6f8/rGYLaq-rmoJJYotkC1VXk.jpeg
Anton Obukhov
user
toshas
66a355b0e88715bf1bb30953
Markus Gross
66a355b0e88715bf1bb30954
Konrad Schindler
claimed_verified
2025-05-15T13:45:48.524Z
6750649b7edd6a98a1bbcd06
/avatars/ba27f12d0333cf2d400d4405af7efe97.svg
Konrad Schindler
user
konradschindler
66a355b0e88715bf1bb30955
Christopher Schroers
BetterDepth: Plug-and-Play Diffusion Refiner for Zero-Shot Monocular
  Depth Estimation
By training over large-scale datasets, zero-shot monocular depth estimation
(MDE) methods show robust performance in the wild but often suffer from
insufficiently precise details. Although recent diffusion-based MDE approaches
exhibit appealing detail extraction ability, they still struggle in
geometrically challenging scenes due to the difficulty of gaining robust
geometric priors from diverse datasets. To leverage the complementary merits of
both worlds, we propose BetterDepth to efficiently achieve geometrically
correct affine-invariant MDE performance while capturing fine-grained details.
Specifically, BetterDepth is a conditional diffusion-based refiner that takes
the prediction from pre-trained MDE models as depth conditioning, in which the
global depth context is well-captured, and iteratively refines details based on
the input image. For the training of such a refiner, we propose global
pre-alignment and local patch masking methods to ensure the faithfulness of
BetterDepth to depth conditioning while learning to capture fine-grained scene
details. By efficient training on small-scale synthetic datasets, BetterDepth
achieves state-of-the-art zero-shot MDE performance on diverse public datasets
and in-the-wild scenes. Moreover, BetterDepth can improve the performance of
other MDE models in a plug-and-play manner without additional re-training.
BetterDepth, a conditional diffusion-based refiner, enhances zero-shot monocular depth estimation by refining pre-trained model predictions with global and local context while efficiently training on synthetic datasets.
zero-shot monocular depth estimation diffusion-based MDE affine-invariant MDE conditional diffusion depth conditioning global depth context local patch masking state-of-the-art zero-shot MDE