# GHOST: Ground-projected Hypotheses from Observed Structure-from-Motion Trajectories

arXiv: https://arxiv.org/abs/2603.20583 · HTML: https://mlsanity.com/paper/2603.20583/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 36 references that are in the map.
- First posted on arXiv 21 Mar 2026.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [Objaverse: A Universe of Annotated 3D Objects](https://mlsanity.com/paper/2212.08051/) (2022)
- [Depth Anything V2](https://mlsanity.com/paper/2406.09414/) (2024)
- [Grounding Image Matching in 3D with MASt3R](https://mlsanity.com/paper/2406.09756/) (2024)
- [Unsupervised Learning of Depth and Ego-Motion from Video](https://mlsanity.com/paper/1704.07813/) (2017)
- [ScanNet++: A High-Fidelity Dataset of 3D Indoor Scenes](https://mlsanity.com/paper/2308.11417/) (2023)
- [Argoverse: 3D Tracking and Forecasting with Rich Maps](https://mlsanity.com/paper/1911.02620/) (2019)
- [Common Objects in 3D: Large-Scale Learning and Evaluation of Real-life 3D Category Reconstruction](https://mlsanity.com/paper/2109.00512/) (2021)
- [DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-based 3D Vision](https://mlsanity.com/paper/2312.16256/) (2023)
- [VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation](https://mlsanity.com/paper/2005.04259/) (2020)
- [MapAnything: Universal Feed-Forward Metric 3D Reconstruction](https://mlsanity.com/paper/2509.13414/) (2025)
- [RGBD Objects in the Wild: Scaling Real-World 3D Object Learning from RGB-D Videos](https://mlsanity.com/paper/2401.12592/) (2024)
- [UnDeepVO: Monocular Visual Odometry through Unsupervised Deep Learning](https://mlsanity.com/paper/1709.06841/) (2017)
- [OpenLane-V2: A Topology Reasoning Benchmark for Unified 3D HD Mapping](https://mlsanity.com/paper/2304.10440/) (2023)
- [Find Your Own Way: Weakly-Supervised Segmentation of Path Proposals for Urban Autonomy](https://mlsanity.com/paper/1610.01238/) (2016)
- [Diffusion-FS: Multimodal Free-Space Prediction via Diffusion for Autonomous Driving](https://mlsanity.com/paper/2507.18763/) (2025)

## Abstract (arXiv)

We present a scalable self-supervised approach for segmenting feasible vehicle trajectories from monocular images for autonomous driving in complex urban environments. Leveraging large-scale dashcam videos, we treat recorded ego-vehicle motion as implicit supervision and recover camera trajectories via monocular structure-from-motion, projecting them onto the ground plane to generate spatial masks of traversed regions without manual annotation. These automatically generated labels are used to train a deep segmentation network that predicts motion-conditioned path proposals from a single RGB image at run time, without explicit modeling of road or lane markings. Trained on diverse, unconstrained internet data, the model implicitly captures scene layout, lane topology, and intersection structure, and generalizes across varying camera configurations. We evaluate our approach on NuScenes, demonstrating reliable trajectory prediction, and further show transfer to an electric scooter platform through light fine-tuning. Our results indicate that large-scale ego-motion distillation yields structured and generalizable path proposals beyond the demonstrated trajectory, enabling trajectory hypothesis estimation via image segmentation.