Map-Free Benchmarking

• Map-free benchmarking is a methodology that evaluates models and systems using datasets and metrics designed without explicit map or structured priors.
• It spans diverse domains like trajectory prediction, visual relocalization, audio retrieval, and satellite analysis, employing metrics such as ADE, FDE, SSIM, and MAE.
• This approach challenges conventional map-based methods and drives improvements in real-world applications where complete mapping information is unavailable.

A map-free benchmark is defined as an evaluation protocol, dataset, or suite of metrics that facilitates rigorous comparison of models, representations, or systems without relying on explicit precomputed maps, structured priors, or external reference geometries. In such setups, models are assessed in conditions that approximate deployment where high-fidelity maps (e.g., HD maps in vehicle navigation, content mappings in audio retrieval, depth maps in vision, or geospatial ground truths in remote sensing) are unavailable or intentionally omitted. Map-free benchmarks stress the intrinsic reasoning, generalization, and representational robustness of algorithms, emphasizing scenarios that preclude or penalize external context injection.

1. Principles and Scope of Map-Free Benchmarking

Map-free benchmarking encompasses diverse domains, including trajectory prediction, visual relocalization, computational imaging, geospatial estimation, audio representation, and large-scale stream processing. The unifying principle is that all evaluation is conducted absent of reference maps, thereby providing measurement of generalization or intrinsic geometric quality rather than map-fitting capacity.

• In motion forecasting, map-free protocols exclude HD lane graphs at inference; models must reason directly from agent trajectories and limited on-board context (Liu et al., 2024).
• In visual relocalization, only a single reference image per scene is provided, without any global 3D reconstruction or scale calibration, challenging both regression-based and matching-based pose estimation algorithms (Arnold et al., 2022).
• For image dehazing, map-free protocols use real haze captured under controlled illumination, paired with ground-truth clear images, eschewing synthetic depth/computed transmission maps for evaluation (Ancuti et al., 2018).
• Geospatial poverty estimation benchmarks are constructed to test direct prediction from raw satellite imagery to aggregate indicators, with no map-based pretraining or explicit map input (Sharma et al., 2024).
• In audio, map-free benchmarks probe retrieval and content identity in single-source signals without source-separation maps or class adaptivity, using label-free metrics and frozen embeddings (Basha et al., 10 Dec 2025).
• In distributed systems, map-free streaming benchmarks such as ShuffleBench intentionally decouple shuffling logic from aggregation, systematically quantifying performance in realistic cloud-native deployments (Henning et al., 2024).

2. Architectural and Evaluation Protocols

The implementation of map-free benchmarks is characterized by careful dataset design to guarantee the absence of map-derived priors at test time and by the selection of evaluation metrics that are robust to the lack of external geometric context.

• Trajectory Prediction Example (MFTP): Input comprises only agents’ relative motion vectors. The evaluation uses the Argoverse dataset with models run strictly without map-polylines at inference. Metrics include Average Displacement Error (ADE) and Final Displacement Error (FDE):

$$\mathrm{ADE} = \frac{1}{T}\sum_{t=1}^T \|\hat y_t - y_t\|_2,\quad \mathrm{FDE} = \|\hat y_T - y_T\|_2$$

• Visual Relocalization Example: Inputs are a reference image $I_{\rm ref}$ and a query $I_q$; output is a 6-DoF rigid transform $(\hat R,\hat t)$ mapping world coordinates. Protocols evaluate:

$$\Delta_{\rm trans} = \|\hat t-t\|_2,\quad \Delta_{\rm rot} = \cos^{-1}\left(\frac{\mathrm{trace}(\hat R R^T)-1}{2}\right)$$

together with AR-style virtual correspondence reprojection error (VCRE) (Arnold et al., 2022).

• Audio Example (VocSim): No labels or class boundaries are used at any stage except for metric computation. Evaluation includes:
  • Precision@k for retrieval:

  $$P@k = \frac{1}{N \cdot k} \sum_{i=1}^N \sum_{j \in \mathcal{N}_k(i)} \mathbb{I}(y_i = y_j)$$ - Global Separation Rate (GSR):

  $$s_i = \frac{\mathrm{NID}_i - \mathrm{Avg\_ID}_i}{\mathrm{NID}_i + \mathrm{Avg\_ID}_i + \varepsilon}$$

3. Dataset Construction and Map-Free Design Controls

Robust map-free benchmarking necessitates explicit control over scene, label, and signal variability, and careful avoidance of back-door injection of geometric context.

• Indoor Dehazing (I-HAZE): Dataset pairs hazy images generated with professional haze machines and exact haze-free ground truth, constant camera parameters, embedded MacBeth color checker for calibration, and strict isolation from depth maps or synthesized transmission fields. Evaluation uses SSIM, PSNR, and CIEDE2000 color difference without reliance on synthetic geometry (Ancuti et al., 2018).
• Satellite Poverty Estimation (KidSat): Map-free evaluation is assured by using only raw Sentinel/Landsat tiles centered on survey clusters, paired with multidimensional poverty indicators computed from survey microdata. Explicitly, ensemble learning is restricted to using imagery for prediction, omitting ancillary map-derived features. Metrics are mean absolute error (MAE) on cluster-level severe deprivation rates (Sharma et al., 2024).
• Audio Retrieval (VocSim): Classes index concrete acoustic units with precise temporal boundaries, aggregated across 19 diverse corpora, with single-source restriction enforced to isolate content identity (Basha et al., 10 Dec 2025).

4. Performance Metrics, Result Analysis, and Comparative Findings

Map-free benchmarks typically report metrics that directly reflect intrinsic model performance in the absence of map context—often exposing a significant generalization gap versus map-based or context-enriched evaluations.

Domain/Benchmark	Primary Metric(s)	SOTA Map-Free Result	Generalization Observations
Motion Forecast (MFTP)	minADE₆, minFDE₆	0.84 m, 1.38 m	Map distillation improves, but inference map-free is essential (Liu et al., 2024)
Relocalization	VCRE (%) recall, Δrot	~30% recall @ 5% diag	Matching+depth stronger under overlap, regression yields coarse fallback (Arnold et al., 2022)
Dehazing (I-HAZE)	SSIM, PSNR, CIEDE2000	SSIM=0.791, PSNR=17.28	CNN-based Ren et al. most robust, no method excels universally (Ancuti et al., 2018)
Poverty (KidSat)	MAE (%)	MAE=0.1836 (Sentinel)	Foundation models > satellite-specific, overfitting harms temporal transfer (Sharma et al., 2024)
Audio Retrieval (VocSim)	P@1, GSR (%)	P@1=66.8%, GSR=41.7	Blind speech subsets reveal geometric collapse (P@1=11.5%) (Basha et al., 10 Dec 2025)

Performance analysis reveals that certain map-free architectures (e.g., hierarchical encoders with knowledge distillation in MFTP, label-free PCA in Audio) can transfer structured priors to the map-free inference context. However, fully map-free, zero-shot generalization remains a challenge across modalities, as evidenced by sharp drops in local retrieval for OOD audio and translation error inflation when monocular depths fail under relocalization.

5. Impact and Use Cases

Map-free benchmarks play a pivotal role in the evaluation and selection of models for deployment environments where reliable, up-to-date maps are unobtainable due to scale, cost, privacy, or dynamic topology. Notable use cases include:

• Real-time decision-making for autonomous vehicles in construction zones or with incomplete HD-map coverage (Liu et al., 2024).
• AR/VR localization in novel or transient environments using only sparse image anchors (Arnold et al., 2022).
• Statistical analysis of poverty and socio-economic outcomes from up-to-date satellite imagery without extrapolating from legacy geo-maps (Sharma et al., 2024).
• Dehazing in rapidly changing indoor or outdoor scenes, forensics, or historical restoration (Ancuti et al., 2018).
• Fast, zero-shot audio retrieval, clustering, and biological perceptual modeling in languages, species, or sound domains with no labeled corpus (Basha et al., 10 Dec 2025).
• Infrastructure benchmarking for distributed stream processing (e.g., ShuffleBench)—diagnosing network/serialization bottlenecks with domain-independent, map-free workflows (Henning et al., 2024).

6. Extensions, Limitations, and Future Directions

Map-free benchmarking exposes several empirical challenges and open research directions:

• The generalized collapse in feature separability in blind or OOD regimes (e.g., unseen languages in audio) suggests current models interpolate rather than extrapolate content structure; future work may focus on unsupervised whitening, ICA, or new encoder priors (Basha et al., 10 Dec 2025).
• Knowledge distillation from map-based teachers to map-free students (as in MFTP) offers improved accuracy, but inference robustness depends on transferability of the learned representation (Liu et al., 2024).
• Community benchmarking efforts recommend extending map-free evaluation protocols into temporal domains (video), hybrid approaches (fusion of correspondence and regression), and into practical scenarios with secure evaluation for sensitive corpora (Ancuti et al., 2018, Basha et al., 10 Dec 2025).
• As practical deployments increasingly decouple from costly mapping pipelines and global reference datasets, map-free benchmarks are expected to serve as standardized acceptance tests and leaderboard systems, aligning model selection with real-world constraints.

7. Open-Source Resources and Standardization

Several map-free benchmarks reviewed herein provide comprehensive, open-source toolkits, datasets, and reproducible pipelines for unrestricted research:

• ShuffleBench: Kubernetes-based deployment scripts and metrics automation (Henning et al., 2024).
• MFTP: Implementation with knowledge distillation for map-free trajectory prediction (Liu et al., 2024).
• Visual Relocalization: Dataset, baseline code, and online evaluation server (Arnold et al., 2022).
• I-HAZE: Full release of raw/demosaiced indoor dehazing images and metrics scripts (Ancuti et al., 2018).
• KidSat: End-to-end scripts for satellite tile extraction, survey aggregation, and benchmark training (Sharma et al., 2024).
• VocSim: Aggregated audio corpora, pipeline, and secure leaderboard for standardizing geometric evaluation (Basha et al., 10 Dec 2025).

The proliferation of map-free benchmarks responds to the research community's need for reproducibility, realism, and transferability in algorithm evaluation, providing an increasingly standardized foundation for future work across modalities and applications.