# Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes

arXiv: https://arxiv.org/abs/1908.06316 · HTML: https://mlsanity.com/paper/1908.06316/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Aug 2019.

## Built on by (most cited first, 27 of 27)

- [Neural Scene Flow Prior](https://mlsanity.com/paper/2111.01253/) (2021)
- [Learning to Segment Rigid Motions from Two Frames](https://mlsanity.com/paper/2101.03694/) (2021)
- [Fast Neural Scene Flow](https://mlsanity.com/paper/2304.09121/) (2023)
- [Learning Optical Flow, Depth, and Scene Flow without Real-World Labels](https://mlsanity.com/paper/2203.15089/) (2022)
- [Self-Supervised Multi-Frame Monocular Scene Flow](https://mlsanity.com/paper/2105.02216/) (2021)
- [RPEFlow: Multimodal Fusion of RGB-PointCloud-Event for Joint Optical Flow and Scene Flow Estimation](https://mlsanity.com/paper/2309.15082/) (2023)
- [Learning to Fuse Monocular and Multi-view Cues for Multi-frame Depth Estimation in Dynamic Scenes](https://mlsanity.com/paper/2304.08993/) (2023)
- [Zero-Shot Monocular Scene Flow Estimation in the Wild](https://mlsanity.com/paper/2501.10357/) (2025)
- [Re-Evaluating LiDAR Scene Flow for Autonomous Driving](https://mlsanity.com/paper/2304.02150/) (2023)
- [Bring Event into RGB and LiDAR: Hierarchical Visual-Motion Fusion for Scene Flow](https://mlsanity.com/paper/2403.07432/) (2024)
- [RMS-FlowNet: Efficient and Robust Multi-Scale Scene Flow Estimation for Large-Scale Point Clouds](https://mlsanity.com/paper/2204.00354/) (2022)
- [D3FlowSLAM: Self-Supervised Dynamic SLAM with Flow Motion Decomposition and DINO Guidance](https://mlsanity.com/paper/2207.08794/) (2022)
- [RAFT-MSF: Self-Supervised Monocular Scene Flow using Recurrent Optimizer](https://mlsanity.com/paper/2205.01568/) (2022)
- [3D Scene Flow Estimation on Pseudo-LiDAR: Bridging the Gap on Estimating Point Motion](https://mlsanity.com/paper/2209.13130/) (2022)
- [Attention-based Dual Supervised Decoder for RGBD Semantic Segmentation](https://mlsanity.com/paper/2201.01427/) (2022)
- [Learning to Estimate Single-View Volumetric Flow Motions without 3D Supervision](https://mlsanity.com/paper/2302.14470/) (2023)
- [EMR-MSF: Self-Supervised Recurrent Monocular Scene Flow Exploiting Ego-Motion Rigidity](https://mlsanity.com/paper/2309.01296/) (2023)
- [MonoPLFlowNet: Permutohedral Lattice FlowNet for Real-Scale 3D Scene FlowEstimation with Monocular Images](https://mlsanity.com/paper/2111.12325/) (2021)
- [Multi-view Monocular Depth and Uncertainty Prediction with Deep SfM in Dynamic Environments](https://mlsanity.com/paper/2201.08633/) (2022)
- [Efficient Stereo Depth Estimation for Pseudo LiDAR: A Self-Supervised Approach Based on Multi-Input ResNet Encoder](https://mlsanity.com/paper/2205.08089/) (2022)
- [FedRSU: Federated Learning for Scene Flow Estimation on Roadside Units](https://mlsanity.com/paper/2401.12862/) (2024)
- [EMoTive: Event-guided Trajectory Modeling for 3D Motion Estimation](https://mlsanity.com/paper/2503.11371/) (2025)
- [DEFLOW: Self-supervised 3D Motion Estimation of Debris Flow](https://mlsanity.com/paper/2304.02569/) (2023)
- [StixelNExT: Toward Monocular Low-Weight Perception for Object Segmentation and Free Space Detection](https://mlsanity.com/paper/2407.08277/) (2024)
- [ZeST-NeRF: Using temporal aggregation for Zero-Shot Temporal NeRFs](https://mlsanity.com/paper/2311.18491/) (2023)
- [SF3D-RGB: Scene Flow Estimation from Monocular Camera and Sparse LiDAR](https://mlsanity.com/paper/2602.21699/) (2026)
- [RAFT-MSF++: Temporal Geometry-Motion Feature Fusion for Self-Supervised Monocular Scene Flow](https://mlsanity.com/paper/2604.19349/) (2026)

## Abstract (arXiv)

Existing 3D scene flow estimation methods provide the 3D geometry and 3D motion of a scene and gain a lot of interest, for example in the context of autonomous driving. These methods are traditionally based on a temporal series of stereo images. In this paper, we propose a novel monocular 3D scene flow estimation method, called Mono-SF. Mono-SF jointly estimates the 3D structure and motion of the scene by combining multi-view geometry and single-view depth information. Mono-SF considers that the scene flow should be consistent in terms of warping the reference image in the consecutive image based on the principles of multi-view geometry. For integrating single-view depth in a statistical manner, a convolutional neural network, called ProbDepthNet, is proposed. ProbDepthNet estimates pixel-wise depth distributions from a single image rather than single depth values. Additionally, as part of ProbDepthNet, a novel recalibration technique for regression problems is proposed to ensure well-calibrated distributions. Our experiments show that Mono-SF outperforms state-of-the-art monocular baselines and ablation studies support the Mono-SF approach and ProbDepthNet design.