# SVIRO: Synthetic Vehicle Interior Rear Seat Occupancy Dataset and Benchmark

arXiv: https://arxiv.org/abs/2001.03483 · HTML: https://mlsanity.com/paper/2001.03483/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jan 2020.

## Built on by (most cited first, 21 of 21)

- [Sparse Mixture-of-Experts are Domain Generalizable Learners](https://mlsanity.com/paper/2206.04046/) (2022)
- [Assaying Out-Of-Distribution Generalization in Transfer Learning](https://mlsanity.com/paper/2207.09239/) (2022)
- [TICaM: A Time-of-flight In-car Cabin Monitoring Dataset](https://mlsanity.com/paper/2103.11719/) (2021)
- [Grounding Stylistic Domain Generalization with Quantitative Domain Shift Measures and Synthetic Scene Images](https://mlsanity.com/paper/2405.15961/) (2024)
- [A Language Anchor-Guided Method for Robust Noisy Domain Generalization](https://mlsanity.com/paper/2503.17211/) (2025)
- [Multitask Network for Joint Object Detection, Semantic Segmentation and Human Pose Estimation in Vehicle Occupancy Monitoring](https://mlsanity.com/paper/2205.01515/) (2022)
- [Alice Benchmarks: Connecting Real World Re-Identification with the Synthetic](https://mlsanity.com/paper/2310.04416/) (2023)
- [Autoencoder Based Inter-Vehicle Generalization for In-Cabin Occupant Classification](https://mlsanity.com/paper/2105.03164/) (2021)
- [Finding Competence Regions in Domain Generalization](https://mlsanity.com/paper/2303.09989/) (2023)
- [Autoencoder Attractors for Uncertainty Estimation](https://mlsanity.com/paper/2204.00382/) (2022)
- [Supporting Safety Analysis of Image-processing DNNs through Clustering-based Approaches](https://mlsanity.com/paper/2301.13506/) (2023)
- [Light-Weight Vision Transformer with Parallel Local and Global Self-Attention](https://mlsanity.com/paper/2307.09120/) (2023)
- [Background-Foreground Segmentation for Interior Sensing in Automotive Industry](https://mlsanity.com/paper/2109.09410/) (2021)
- [Detection of Driver Drowsiness by Calculating the Speed of Eye Blinking](https://mlsanity.com/paper/2110.11223/) (2021)
- [Boosting Few-Shot Detection with Large Language Models and Layout-to-Image Synthesis](https://mlsanity.com/paper/2410.06841/) (2024)
- [Autoencoder for Synthetic to Real Generalization: From Simple to More Complex Scenes](https://mlsanity.com/paper/2204.00386/) (2022)
- [Virtual passengers for real car solutions: synthetic datasets](https://mlsanity.com/paper/2205.06556/) (2022)
- [AutoExp: A multidisciplinary, multi-sensor framework to evaluate human activities in self-driving cars](https://mlsanity.com/paper/2306.03115/) (2023)
- [Clustering-Based Validation Splits for Model Selection under Domain Shift](https://mlsanity.com/paper/2405.19461/) (2024)
- [Texture-Shape Bias Balancing for Robust Synthetic-to-Real Semantic Segmentation in Automotive NIR Imagery](https://mlsanity.com/paper/2606.15072/) (2026)
- [Search-based Testing of Vision Language Models for In-Car Scene Understanding](https://mlsanity.com/paper/2607.02300/) (2026)

## Abstract (arXiv)

We release SVIRO, a synthetic dataset for sceneries in the passenger compartment of ten different vehicles, in order to analyze machine learning-based approaches for their generalization capacities and reliability when trained on a limited number of variations (e.g. identical backgrounds and textures, few instances per class). This is in contrast to the intrinsically high variability of common benchmark datasets, which focus on improving the state-of-the-art of general tasks. Our dataset contains bounding boxes for object detection, instance segmentation masks, keypoints for pose estimation and depth images for each synthetic scenery as well as images for each individual seat for classification. The advantage of our use-case is twofold: The proximity to a realistic application to benchmark new approaches under novel circumstances while reducing the complexity to a more tractable environment, such that applications and theoretical questions can be tested on a more challenging dataset as toy problems. The data and evaluation server are available under https://sviro.kl.dfki.de.