mlsanityData to 14 Oct 2026

MonoPLFlowNet: Permutohedral Lattice FlowNet for Real-Scale 3D Scene FlowEstimation with Monocular Images

arXiv 2111.12325 ↗First posted 24 Nov 2021cs.CVcited by 2

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Abstract (arXiv)

Real-scale scene flow estimation has become increasingly important for 3D computer vision. Some works successfully estimate real-scale 3D scene flow with LiDAR. However, these ubiquitous and expensive sensors are still unlikely to be equipped widely for real application. Other works use monocular images to estimate scene flow, but their scene flow estimations are normalized with scale ambiguity, where additional depth or point cloud ground truth are required to recover the real scale. Even though they perform well in 2D, these works do not provide accurate and reliable 3D estimates. We present a deep learning architecture on permutohedral lattice - MonoPLFlowNet. Different from all previous works, our MonoPLFlowNet is the first work where only two consecutive monocular images are used as input, while both depth and 3D scene flow are estimated in real scale. Our real-scale scene flow estimation outperforms all state-of-the-art monocular-image based works recovered to real scale by ground truth, and is comparable to LiDAR approaches. As a by-product, our real-scale depth estimation also outperforms other state-of-the-art works.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 36 of its 56 references that are in the map. First posted on arXiv 24 Nov 2021.

Builds on

36 of 56 references.

References · 36

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24 more
  1. 2018GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Posecited by 342
  2. 2018FlowNet3D: Learning Scene Flow in 3D Point Cloudscited by 242
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  10. 2019HPLFlowNet: Hierarchical Permutohedral Lattice FlowNet for Scene Flow Estimation on Large-scale Point Cloudscited by 109
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  12. 2019Just Go with the Flow: Self-Supervised Scene Flow Estimationcited by 98
  13. 2020Self-Supervised Monocular Scene Flow Estimationcited by 64
  14. 2019FlowNet3D++: Geometric Losses For Deep Scene Flow Estimationcited by 55
  15. 2019Deep Rigid Instance Scene Flowcited by 54
  16. 2020Self-Supervised Learning of Non-Rigid Residual Flow and Ego-Motioncited by 41
  17. 2018Learning Rigidity in Dynamic Scenes with a Moving Camera for 3D Motion Field Estimationcited by 38
  18. 2019SENSE: a Shared Encoder Network for Scene-flow Estimationcited by 38
  19. 2021Self-Supervised Multi-Frame Monocular Scene Flowcited by 29
  20. 2019Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenescited by 27
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  22. 2017Fast Multi-frame Stereo Scene Flow with Motion Segmentationcited by 6
  23. 2017SceneFlowFields: Dense Interpolation of Sparse Scene Flow Correspondencescited by 6
  24. 2019Learning Task-Specific Generalized Convolutions in the Permutohedral Latticecited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 1.

2 papers.

  1. 2024SplatSDF: Boosting SDF-NeRF via Architecture-Level Fusion with Gaussian Splatscited by 5
  2. 2026DiFF: Doppler-informed Flow Matching for Human Motion Flowcited by 0