# The Earth ain't Flat: Monocular Reconstruction of Vehicles on Steep and Graded Roads from a Moving Camera

arXiv: https://arxiv.org/abs/1803.02057 · HTML: https://mlsanity.com/paper/1803.02057/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2018.

## Built on by (most cited first, 11 of 11)

- [Is Pseudo-Lidar needed for Monocular 3D Object detection?](https://mlsanity.com/paper/2108.06417/) (2021)
- [RODNet: A Real-Time Radar Object Detection Network Cross-Supervised by Camera-Radar Fused Object 3D Localization](https://mlsanity.com/paper/2102.05150/) (2021)
- [MonoNeRD: NeRF-like Representations for Monocular 3D Object Detection](https://mlsanity.com/paper/2308.09421/) (2023)
- [OPA-3D: Occlusion-Aware Pixel-Wise Aggregation for Monocular 3D Object Detection](https://mlsanity.com/paper/2211.01142/) (2022)
- [Rethinking of Radar's Role: A Camera-Radar Dataset and Systematic Annotator via Coordinate Alignment](https://mlsanity.com/paper/2105.05207/) (2021)
- [Viewpoint Equivariance for Multi-View 3D Object Detection](https://mlsanity.com/paper/2303.14548/) (2023)
- [Multi-Target Multi-Camera Tracking of Vehicles using Metadata-Aided Re-ID and Trajectory-Based Camera Link Model](https://mlsanity.com/paper/2105.01213/) (2021)
- [Depth Is All You Need for Monocular 3D Detection](https://mlsanity.com/paper/2210.02493/) (2022)
- [Probabilistic Vehicle Reconstruction Using a Multi-Task CNN](https://mlsanity.com/paper/2102.10681/) (2021)
- [Pose Estimation and 3D Reconstruction of Vehicles from Stereo-Images Using a Subcategory-Aware Shape Prior](https://mlsanity.com/paper/2107.10898/) (2021)
- [NMR: Neural Manifold Representation for Autonomous Driving](https://mlsanity.com/paper/2205.05551/) (2022)

## Abstract (arXiv)

Accurate localization of other traffic participants is a vital task in autonomous driving systems. State-of-the-art systems employ a combination of sensing modalities such as RGB cameras and LiDARs for localizing traffic participants, but most such demonstrations have been confined to plain roads. We demonstrate, to the best of our knowledge, the first results for monocular object localization and shape estimation on surfaces that do not share the same plane with the moving monocular camera. We approximate road surfaces by local planar patches and use semantic cues from vehicles in the scene to initialize a local bundle-adjustment like procedure that simultaneously estimates the pose and shape of the vehicles, and the orientation of the local ground plane on which the vehicle stands as well. We evaluate the proposed approach on the KITTI and SYNTHIA-SF benchmarks, for a variety of road plane configurations. The proposed approach significantly improves the state-of-the-art for monocular object localization on arbitrarily-shaped roads.