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NeW CRFs: Neural Window Fully-connected CRFs for Monocular Depth Estimation

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arxiv 2203.01502 v2 pith:YVNNK2JN submitted 2022-03-03 cs.CV

NeW CRFs: Neural Window Fully-connected CRFs for Monocular Depth Estimation

Weihao Yuan , Xiaodong Gu , Zuozhuo Dai , Siyu Zhu , Ping Tan This is my paper
classification cs.CV
keywords crfsdepthfc-crfswindowcomputationdirectlyfully-connectedgraph
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Estimating the accurate depth from a single image is challenging since it is inherently ambiguous and ill-posed. While recent works design increasingly complicated and powerful networks to directly regress the depth map, we take the path of CRFs optimization. Due to the expensive computation, CRFs are usually performed between neighborhoods rather than the whole graph. To leverage the potential of fully-connected CRFs, we split the input into windows and perform the FC-CRFs optimization within each window, which reduces the computation complexity and makes FC-CRFs feasible. To better capture the relationships between nodes in the graph, we exploit the multi-head attention mechanism to compute a multi-head potential function, which is fed to the networks to output an optimized depth map. Then we build a bottom-up-top-down structure, where this neural window FC-CRFs module serves as the decoder, and a vision transformer serves as the encoder. The experiments demonstrate that our method significantly improves the performance across all metrics on both the KITTI and NYUv2 datasets, compared to previous methods. Furthermore, the proposed method can be directly applied to panorama images and outperforms all previous panorama methods on the MatterPort3D dataset. Project page: https://weihaosky.github.io/newcrfs.

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Forward citations

Cited by 9 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Adaptive Depth-converted-Scale Convolution for Self-supervised Monocular Depth Estimation

    cs.CV 2026-04 unverdicted novelty 7.0

    DcSConv adapts convolution filter scales based on object depth to reduce size-depth ambiguity in self-supervised monocular depth estimation, improving performance on KITTI by up to 11.6% in SqRel.

  2. Efficient Test-Time Optimization for Depth Completion via Low-Rank Decoder Adaptation

    cs.CV 2026-03 unverdicted novelty 7.0

    Low-rank decoder adaptation enables efficient test-time optimization for zero-shot depth completion by updating only the subspace containing depth-relevant information.

  3. ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth

    cs.CV 2023-02 accept novelty 7.0

    ZoeDepth combines relative depth pre-training on many datasets with metric depth fine-tuning and automatic head routing to achieve strong zero-shot generalization while preserving metric scale.

  4. The Multipath Blind Spot: $K$-Agnostic Robust Calibration for Sparse-Anchor Metric Depth from Frozen Foundations

    cs.CV 2026-07 accept novelty 6.5

    MRAC gates sparse anchors via Theil–Sen + MAD consistency with a frozen foundation's relative depth, repairing multipath outliers that collapse residual-on-CFA and blind VI-Depth while winning 84% of same-backbone cells.

  5. Monocular Depth Estimation From the Perspective of Feature Restoration: A Diffusion Enhanced Depth Restoration Approach

    cs.CV 2026-04 conditional novelty 6.0

    Monocular depth estimation is recast as indirect feature restoration via an invertible diffusion module plus auxiliary viewpoint enhancement, delivering 4-38% RMSE gains on KITTI over baselines.

  6. Depth Anything V2

    cs.CV 2024-06 unverdicted novelty 6.0

    Depth Anything V2 delivers finer, more robust monocular depth predictions by replacing real labeled images with synthetic data, scaling the teacher model, and using large-scale pseudo-labeled real images for student training.

  7. Reinforcing Egocentric Spatial Perception in Multimodal Large Language Models via Ego Scene Augmentation

    cs.CV 2026-07 conditional novelty 5.0

    Ego Scene Augmentation boosts egocentric VQA accuracy by 8.14% (indoor) and 8.72% (outdoor) by injecting a Depth-Anything-derived object/depth/text scene graph into the MLLM prompt.

  8. Boosting Monocular Metric Depth Estimation via Bokeh Rendering

    cs.CV 2025-12 reject novelty 5.0

    A two-stage method that synthesizes bokeh stacks from one image and uses them to boost the metric accuracy of monocular depth estimation.

  9. DepthMaster: Taming Diffusion Models for Monocular Depth Estimation

    cs.CV 2025-01 unverdicted novelty 5.0

    DepthMaster proposes a single-step diffusion model with Feature Alignment and Fourier Enhancement modules in a two-stage training process to improve generalization and detail preservation in monocular depth estimation...