OPENNAVMAP Map-Free Benchmarks

• OPENNAVMAP is a set of benchmarking methodologies that assess model performance without relying on explicit ground-truth maps or spatial priors.
• The benchmarks span domains like autonomous driving, visual localization, and image restoration, using metrics such as minADE, SSIM, and MAE to evaluate performance.
• Results from map-free evaluations expose generalization challenges and drive innovations in hybrid models that learn to synthesize minimal contextual priors for real-world applications.

OPENNAVMAP refers to a class of benchmarking and evaluation methodologies that deliberately exclude explicit ground-truth mapping data—such as High-Definition (HD) maps, transmission maps, or labeled scene reconstructions—during inference, focusing instead on measuring model performance under “map-free” constraints. In these benchmarks, evaluation and/or deployment proceed without access to traditional spatial or environmental priors, challenging algorithms to operate in scenarios where such detailed maps are unavailable, unreliable, or irrelevant. Map-free benchmarks have become crucial in domains including autonomous driving, robotic perception, image restoration, remote sensing, visual localization, audio understanding, and distributed systems.

1. Conceptual Scope and Motivation

The core principle of OPENNAVMAP-style (map-free) benchmarks is to evaluate the capacity of models to generalize and function robustly without reliance on externally provided, environment-specific priors at test time. While “maps” can take various forms—annotated scene layouts, HD road graphs, depth/transmission information, or transmission matrices—map-free protocols restrict access to these at inference, either for reasons of practicality (cost, coverage, recency), realism (dynamic environments), or to enable universal, scalable solutions. A common motif is permitting the use of map or context data during training, but withholding it during evaluation—for example, map distillation into a map-free model. This distinguishes map-free approaches from fully map-based pipelines and from synthetic-data-only benchmarks.

2. Exemplar Map-Free Benchmarks Across Domains

A diverse set of map-free benchmarks has emerged:

• Autonomous Driving and Trajectory Prediction:
  • The MFTP method on the Argoverse 1.0 dataset exemplifies “map-free” trajectory prediction by withholding HD maps during inference while exploiting teacher-student distillation from a map-based teacher during training. Agent past motions and inter-agent context are the sole online priors. Metrics include minADE$_K$, minFDE$_K$, MR$_K$, Brier-minFDE$_K$, and Drivable Area Compliance (DAC$_K$). MFTP achieves 0.84 minADE$_6$, 1.38 minFDE$_6$, 0.16 MR$_6$ on test (Liu et al., 2024).
• Visual Relocalization:
  • The Niantic Map-Free Relocalization Dataset benchmarks the estimation of a query’s 6-DOF camera pose relative to a single reference image, with no scene-wide mapping. Baselines split into relative pose regression and geometry with monocular depth. The protocol evaluates median errors and Virtual Correspondence Reprojection Error (VCRE), strictly under a K=1 “database frame” scenario (Arnold et al., 2022).
• Image Dehazing:
  • The I-HAZE dataset offers map-free benchmarking for single-image dehazing by capturing paired real hazy and haze-free indoor images with precise pixel correspondence, but without depth or transmission maps. This enables direct evaluation via PSNR, SSIM, and CIEDE2000 without reliance on synthetic ground-truth maps (Ancuti et al., 2018).
• Remote Sensing and Socio-Economic Prediction:
  • KidSat pairs satellite imagery with socio-economic outcomes (child poverty) and benchmarks models without explicit geospatial segmentation or map priors during evaluation. All mapping between input images and poverty rates occurs via learning, not ground-truth land cover maps or shape files. Metrics include MAE and cross-validated splits (Sharma et al., 2024).
• Audio Representation:
  • VocSim operates as a zero-shot, map-free audio benchmark for probing the intrinsic geometry of frozen audio encoders, evaluating local purity (Precision@k) and the Global Separation Rate (GSR) in the absence of label-based fine-tuning or content maps. External downstream tasks further validate benchmark relevance (Basha et al., 10 Dec 2025).
• Distributed Stream Processing Systems:
  • ShuffleBench restricts aggregation logic to black-box stateful consumers and benchmarks only the data redistribution (“shuffle”) stage, abstracting away any dependence on application-specific maps of aggregation state. Throughput and latency are measured in a framework-independent, map-free setup (Henning et al., 2024).

3. Evaluation Metrics and Protocols

OPENNAVMAP benchmarks consistently encode well-defined test-only constraints and formally specified evaluation metrics suited to the operational semantics of each domain. Examples include:

• Trajectory Forecasting: $\minADE_K$, $\minFDE_K$, MR$_K$, Brier-minFDE$_K$, DAC$_K$ (Liu et al., 2024).
• Relocalization: Rotation/translation error, VCRE with percent-diagonal acceptance (Arnold et al., 2022).
• Image Restoration: PSNR, SSIM, CIEDE2000, calculated over pixel-aligned pairs (Ancuti et al., 2018).
• Regression/Baseline Models: Mean Absolute Error, Root Mean Squared Error, $R^2$ (Sharma et al., 2024).
• Audio Embedding: Precision@k, GSR, and difference-to-permutation baselines (Basha et al., 10 Dec 2025).
• Stream Processing: Sustainable throughput, ad-hoc throughput, $p$-percentile end-to-end latency, resource scalability functions (Henning et al., 2024).

Protocols typically enforce strict cross-validation, temporal or spatial split discipline, and report results as means with confidence intervals or standard error, minimizing dataset overfit and maximizing robust, transferable insights.

4. Architectural and Methodological Implications

Operating under map-free constraints has driven domain-specific innovations:

• Distillation and Hierarchical Encoding: Knowledge distillation from map-aware teachers to map-free students (e.g., MFTP) injects compressed prior structure via additional feature- and query-level losses, enhancing inference robustness without violating test-time priors (Liu et al., 2024).
• Monocular Geometry and Learned Priors: In visual relocalization, the absence of global maps necessitates improved monocular depth estimation and hybrid approaches that fuse regression-based learnt priors with robust geometric feature matching (Arnold et al., 2022).
• Paired Acquisition without Depth/Transmission Maps: For low-level image restoration, map-free benchmarks require strict alignment and calibration but avoid ambiguities and artifacts from synthetic maps, resulting in more reliable structural and colorimetric evaluations (Ancuti et al., 2018).
• Black-Box, Modular Protocols: Benchmarks such as ShuffleBench enforce map-free evaluation by treating aggregation logic as a black box, isolating system-level properties like shuffling efficacy, permitting objective, reproducible system comparison (Henning et al., 2024).

A plausible implication is that architectures developed and validated under map-free protocols tend to generalize better in real-world, map-sparse, or dynamic environments.

5. Comparative Results and Benchmarking Tables

Map-free benchmarks often reveal distinct performance hierarchies and generalization properties:

Table: Representative Map-Free Benchmark Results

Domain	Task	Top Model / Protocol	Primary Metric(s)	Result(s)
Trajectory Prediction	Argoverse/Map-Free	MFTP w/distillation (Liu et al., 2024)	minADE$_6$, MR$_6$	0.84, 0.16 (test)
Visual Relocalization	Niantic Map-Free Dataset	SuperGlue+Depth (geometry), ResUNet (RPR) (Arnold et al., 2022)	VCRE < 5%/10%	~30% / ~50% precision
Dehazing	I-HAZE	Ren et al. (2016)	SSIM, PSNR	0.791, 17.28 dB
Poverty Mapping	KidSat/Spatial	DINOv2 FT (Sentinel) (Sharma et al., 2024)	MAE	0.1836 ± 0.0036
Audio Embedding	VocSim	Whisper-Large-v3 EWMTF+PCA (Basha et al., 10 Dec 2025)	P@1, GSR	66.8%, 41.7% (public subsets)
Stream Processing	ShuffleBench	Flink, Hazelcast Jet (Henning et al., 2024)	Sust. Thruput, L$_{95}$	0.92M/s, 88ms; 0.61M/s, 8ms

These results indicate that state-of-the-art vision and machine learning methods can close the gap between map-based and map-free performance in several domains, though absolute performance drops and OOD generalization issues are significant, particularly when inference relies solely on local or learned context.

6. Significance, Limitations, and Outlook

OPENNAVMAP-style (map-free) benchmarking achieves several significant outcomes:

• Realism and Scalability: Accurately reflects operational settings with incomplete, outdated, or unavailable mapping information.
• Bias Reduction: Avoids algorithmic overfit and artifacts stemming from reliance on synthetic or incomplete maps.
• Generalization Diagnostics: Exposes model overfit to proprietary or static priors, supporting evaluation of genuine representation capacity.
• System-Level Evaluation: Isolates key system properties, such as scalability and shuffling efficiency, without interference from integrated domain logic.

However, limitations include the loss of upper-bound performance (compared to map-rich setups), added difficulty in task disambiguation (e.g., in geometric relocalization), and the need for stringent experimental discipline to ensure fairness and comparability. A plausible implication is that progress in map-free benchmarks will drive the development of hybrid models able to synthesize, distill, or reconstruct essential priors from minimal online observations, potentially closing the gap between fully map-based and deployment-ready systems.