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DeepV2D: Video to Depth with Differentiable Structure from Motion

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arxiv 1812.04605 v4 pith:ZCUWQRJV submitted 2018-12-11 cs.CV

Zachary Teed , Jia Deng This is my paper
classification cs.CV
keywords deepv2ddepthestimationmotionarchitecturedifferentiableend-to-endgeometric
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We propose DeepV2D, an end-to-end deep learning architecture for predicting depth from video. DeepV2D combines the representation ability of neural networks with the geometric principles governing image formation. We compose a collection of classical geometric algorithms, which are converted into trainable modules and combined into an end-to-end differentiable architecture. DeepV2D interleaves two stages: motion estimation and depth estimation. During inference, motion and depth estimation are alternated and converge to accurate depth. Code is available https://github.com/princeton-vl/DeepV2D.

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Cited by 5 Pith papers

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

  1. Glob3R: Global Structure-from-Motion with 3D Foundation Models

    cs.CV 2026-07 conditional novelty 6.0 of 10

    A frozen Pi3X backbone plus dense warping tracks and keyframe sliding-window global optimization yields more accurate, scalable SfM than feed-forward or classical baselines alone.

  2. Test3R: Learning to Reconstruct 3D at Test Time

    cs.CV 2025-06 conditional novelty 6.0 of 10

    Test3R improves 3D reconstruction by optimizing visual prompts at test time so that pointmaps from different image pairs are geometrically consistent.

  3. HAMSt3R: Human-Aware Multi-view Stereo 3D Reconstruction

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    HAMSt3R adds human instance segmentation and DensePose heads to MASt3R, producing feed-forward 3D point maps with per-point human semantics from sparse uncalibrated images.

  4. UAV4D: Dynamic Neural Rendering of Human-Centric UAV Imagery using Gaussian Splatting

    cs.CV 2025-06 conditional novelty 5.0 of 10

    UAV4D reconstructs 4D scenes from monocular drone video by fitting a single global scale to align human meshes with the background mesh, then renders with separate Gaussian splats.

  5. Reconstructing 4D Spatial Intelligence: A Survey

    cs.CV 2025-07 accept novelty 4.0 of 10

    A review that classifies 4D scene reconstruction methods into five progressive levels: low-level cues, scene components, dynamic scenes, interactions, and physics.

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