# SAMP: Shape and Motion Priors for 4D Vehicle Reconstruction

arXiv: https://arxiv.org/abs/2005.00922 · HTML: https://mlsanity.com/paper/2005.00922/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2020.

## Built on by (most cited first, 19 of 19)

- [AutoRF: Learning 3D Object Radiance Fields from Single View Observations](https://mlsanity.com/paper/2204.03593/) (2022)
- [Motion Inspired Unsupervised Perception and Prediction in Autonomous Driving](https://mlsanity.com/paper/2210.08061/) (2022)
- [Exploring Adversarial Robustness of Multi-Sensor Perception Systems in Self Driving](https://mlsanity.com/paper/2101.06784/) (2021)
- [Topologically-Aware Deformation Fields for Single-View 3D Reconstruction](https://mlsanity.com/paper/2205.06267/) (2022)
- [CADSim: Robust and Scalable in-the-wild 3D Reconstruction for Controllable Sensor Simulation](https://mlsanity.com/paper/2311.01447/) (2023)
- [Reconstructing Objects in-the-wild for Realistic Sensor Simulation](https://mlsanity.com/paper/2311.05602/) (2023)
- [Mending Neural Implicit Modeling for 3D Vehicle Reconstruction in the Wild](https://mlsanity.com/paper/2101.06860/) (2021)
- [MV-DeepSDF: Implicit Modeling with Multi-Sweep Point Clouds for 3D Vehicle Reconstruction in Autonomous Driving](https://mlsanity.com/paper/2309.16715/) (2023)
- [Adv3D: Generating Safety-Critical 3D Objects through Closed-Loop Simulation](https://mlsanity.com/paper/2311.01446/) (2023)
- [Know Your Surroundings: Panoramic Multi-Object Tracking by Multimodality Collaboration](https://mlsanity.com/paper/2105.14683/) (2021)
- [UrbanCAD: Towards Highly Controllable and Photorealistic 3D Vehicles for Urban Scene Simulation](https://mlsanity.com/paper/2411.19292/) (2024)
- [Online Adaptation for Implicit Object Tracking and Shape Reconstruction in the Wild](https://mlsanity.com/paper/2111.12728/) (2021)
- [Segment, Lift and Fit: Automatic 3D Shape Labeling from 2D Prompts](https://mlsanity.com/paper/2407.11382/) (2024)
- [MADrive: Memory-Augmented Driving Scene Modeling](https://mlsanity.com/paper/2506.21520/) (2025)
- [3D Surface Reconstruction in the Wild by Deforming Shape Priors from Synthetic Data](https://mlsanity.com/paper/2302.12883/) (2023)
- [SHIFT3D: Synthesizing Hard Inputs For Tricking 3D Detectors](https://mlsanity.com/paper/2309.05810/) (2023)
- [HybridWorldSim: A Scalable and Controllable High-fidelity Simulator for Autonomous Driving](https://mlsanity.com/paper/2511.22187/) (2025)
- [Physics-informed 4D X-ray image reconstruction from ultra-sparse spatiotemporal data](https://mlsanity.com/paper/2504.03469/) (2025)
- [Revisiting Physically Realizable Adversarial Object Attack against LiDAR-based Detection: Clarifying Problem Formulation and Experimental Protocols](https://mlsanity.com/paper/2507.18457/) (2025)

## Abstract (arXiv)

Inferring the pose and shape of vehicles in 3D from a movable platform still remains a challenging task due to the projective sensing principle of cameras, difficult surface properties e.g. reflections or transparency, and illumination changes between images. In this paper, we propose to use 3D shape and motion priors to regularize the estimation of the trajectory and the shape of vehicles in sequences of stereo images. We represent shapes by 3D signed distance functions and embed them in a low-dimensional manifold. Our optimization method allows for imposing a common shape across all image observations along an object track. We employ a motion model to regularize the trajectory to plausible object motions. We evaluate our method on the KITTI dataset and show state-of-the-art results in terms of shape reconstruction and pose estimation accuracy.