Spatialize: Mapping Data to Spatial Representations

• Spatialize is the process of converting non-spatial data such as signals, texts, and images into representations explicitly indexed by position or geometry.
• It spans diverse applications including physics-based acoustic rendering, learned binaural generation, and GIS-based spatial interpolation for urban mapping.
• This conversion facilitates downstream tasks like navigation, retrieval, localization, and planning by providing structured spatial substrates.

Searching arXiv for papers and systems relevant to “Spatialize”. Spatialize denotes a family of operations that convert signals, measurements, semantics, or instructions into representations that are explicitly indexed by position, direction, geometry, or location. In the literature surveyed here, the term spans physically grounded audio rendering from 3D meshes, text- or vision-guided binaural generation, 3D language and radio fields, ensemble spatial interpolation for sparse measurements, GIS-based cooling-potential mapping, geospatial video analytics, mixed-reality instruction placement, and even the space-qualification of an interferometric payload (Chen et al., 2022, Wang et al., 18 Feb 2025, Qin et al., 2023, Egaña et al., 23 Jul 2025).

1. Conceptual scope

A common structure recurs across these domains. A non-spatial input is given—such as a dry waveform, sparse RSS samples, CLIP region features, unlabeled IoT RSS, or a paper instruction document—and the system constructs an output that is queryable or renderable over space. SoundSpaces 2.0 takes arbitrary 3D scene meshes plus frequency-dependent material properties and produces listener- and microphone-specific RIRs, BRIRs, or ambisonic IRs (Chen et al., 2022). RadSplatter estimates a dense radiomap $R(x)$ from sparse beamspace RSS by extending 3D Gaussian Splatting to radio frequencies (Wang et al., 18 Feb 2025). LangSplat defines a 3D language field $f:\mathbb{R}^3 \to \mathbb{R}^d$ aligned with CLIP semantics (Qin et al., 2023). Spatialize v1.0 implements ensemble spatial interpolation for sparse spatial measurements through stochastic partitioning and local interpolators (Egaña et al., 23 Jul 2025).

This breadth implies that spatialization is not a single algorithmic primitive. In some works it is a rendering problem, in others an interpolation problem, a representation-learning problem, or a systems-integration problem. The shared objective is to make spatial structure explicit enough that downstream operations—navigation, retrieval, localization, planning, or reasoning—can be carried out on a spatial substrate rather than on an unstructured signal.

2. Geometry-based acoustic rendering

In acoustics, spatialization is often implemented as the construction of impulse responses conditioned on scene geometry, materials, source pose, and listener pose. SoundSpaces 2.0 uses a geometry-based propagation engine, RLR-Audio-Propagation, with bidirectional path tracing in $M$ logarithmically spaced frequency bands, Phong-BRDF reflections, stochastic transmission, fast edge diffraction, and spherical-harmonic directional encoding (Chen et al., 2022). Direct sound and early reflections of at most two bounces are treated deterministically, while late reverberation is handled statistically through an energy-time histogram. The resulting pressure IR is then used by convolution,

$y(t) = (x * h)(t),$

with binaural rendering

$$p_L(t) = (x * h_L)(t), \qquad p_R(t) = (x * h_R)(t).$$

The platform supports continuous sampling of positions and orientations, configurable materials and microphones, binaural HRTFs, and interactive-rate rendering. On a real-measurement benchmark in a Replica FRL apartment, the direct-to-reverberant ratio error was reduced from $11.0\ \mathrm{dB}$ in SoundSpaces 1.0 to $0.98\ \mathrm{dB}$, while relative RT60 error remained about $12.4\%$ versus real measurements (Chen et al., 2022).

A related but more deployment-oriented formulation appears in work on interoperable 6-DoF audio engines for the audio metaverse. There, the renderer is organized around audio objects, head-relative coordinates, clustered early reflections, and a room-level “reverberation fingerprint” containing frequency-dependent decay times and normalization (Jot et al., 2021). The low-level API is egocentric, while a higher-level scene and propagation layer computes direct-path rendering, per-object reflection controls, and multi-room sends. Obstacles and reflectors are encoded through compact material parameters, especially Transmission and Reflectivity, so that precomputed and real-time solvers can be blended. This suggests a persistent divide within audio spatialization between geometry-first simulation, where propagation is explicit, and parametric engines, where perceptually salient controls are made portable across platforms.

3. Learned and controllable spatial audio generation

Recent generative models shift spatialization from explicit propagation to learned channel synthesis, but they differ sharply in how much control they expose. MusicHiFi centralizes stereo spatialization in a mono-to-stereo upmixer operating in a mid/side formulation: $$M = \frac{L+R}{2}, \qquad S = \frac{L-R}{2}, \qquad L = M+S, \qquad R = M-S.$$ The model predicts only the side channel $S$, so downmix compatibility is exact by construction, and width control is applied at inference by scaling $f:\mathbb{R}^3 \to \mathbb{R}^d$0 with $f:\mathbb{R}^3 \to \mathbb{R}^d$1, $f:\mathbb{R}^3 \to \mathbb{R}^d$2 (Zhu et al., 2024). On DSD100-test and FMA-small, the mid-channel Mel-D and STFT-D are $f:\mathbb{R}^3 \to \mathbb{R}^d$3 by construction, while ViSQOL reaches $f:\mathbb{R}^3 \to \mathbb{R}^d$4 and inference proceeds at approximately $f:\mathbb{R}^3 \to \mathbb{R}^d$5 to $f:\mathbb{R}^3 \to \mathbb{R}^d$6 RTF on GPU for the M2S stage (Zhu et al., 2024).

Text-guided binaural generation makes control more explicit. TAS models the binaural difference $f:\mathbb{R}^3 \to \mathbb{R}^d$7 rather than the two output channels directly, conditions a latent diffusion model on FLAN-T5 text embeddings and monaural audio embeddings, and reconstructs

$f:\mathbb{R}^3 \to \mathbb{R}^d$8

It supports absolute prompts such as “right, behind, below; 5m away” and relative prompts such as “X is left of Y” or “X is farther than Y,” and augments training with flipped-channel audio through the “Flipper” classifier (Pan et al., 1 Jun 2025). On the SpatialTAS test set, the full model improves FD, FAD, KL, IS, and all reported spatial-understanding metrics relative to Mono-Mono, PseudoBinaural, and ablated variants; on FAIR-Play it reports STFT $f:\mathbb{R}^3 \to \mathbb{R}^d$9, ENV $M$0, WAV $M$1, and SNR $M$2 (Pan et al., 1 Jun 2025).

AudioSpa addresses a different setting: single-source text-guided binaural generation with an additional monaural reference (Feng et al., 16 Feb 2025). A frozen FLAN-T5 encoder provides text tokens, FMHA compresses them into a conditioning vector, and FiLM modulates a time-domain convolutional network. In clean single-source conditions, AudioSpa reports DOA MAE $M$3, ACC $M$4, SDR $M$5, and SISDR $M$6, outperforming WarpNet and BinauralGrad on the reported benchmarks (Feng et al., 16 Feb 2025). The same paper also shows that performance degrades sharply in two-source settings, indicating that text-conditioned event-to-direction binding remains substantially easier in single-source mixtures.

Visual guidance supplies yet another control channel. SpatialV2A introduces a dual-branch conditional flow matching generator and the BinauralVGGSound dataset, with 187k clips, 519h, 309 classes, captions, and visually aligned binaural cues (Wang et al., 21 Jan 2026). Its visual-guided spatialization module extracts horizontal position, area fraction, variance, left-right bias, and shape from sounding-object heatmaps, and injects these as spatial conditioning into left and right branches. The model reports strong spatial metrics, including IACC $M$7, ILD $M$8, ITD $M$9, ISD $y(t) = (x * h)(t),$0, and IPD $y(t) = (x * h)(t),$1, alongside MOS-S $y(t) = (x * h)(t),$2 and MOS-AQ $y(t) = (x * h)(t),$3 (Wang et al., 21 Jan 2026).

STASE makes the decoupling explicit. An instruction-tuned LLM interprets either Description Prompts or Abstract Prompts, optionally queries a RAG template bank, emits a structured spatial plan, and hands it to a physics-based renderer implementing panning, analytic ITD/ILD, or HRTF convolution with room processing (Chi et al., 14 Sep 2025). Here, spatialization is not embedded in latent manipulation but split into semantic interpretation and deterministic rendering. A plausible implication is that such a design is intended less to maximize end-to-end generative novelty than to make psychoacoustic parameters inspectable, editable, and reproducible.

4. Spatialized fields, latent representations, and reasoning

A second major line of work spatializes not waveforms but latent features. LangSplat replaces NeRF-style volumetric rendering of language embeddings with 3D Gaussians carrying low-dimensional scene-specific latent features distilled from CLIP (Qin et al., 2023). After RGB 3D Gaussian Splatting, geometry is frozen and each Gaussian is augmented with language features $y(t) = (x * h)(t),$4 for semantic levels $y(t) = (x * h)(t),$5, where $y(t) = (x * h)(t),$6 in the reported setup. Tile-based splatting yields rendered feature maps, which are decoded back to CLIP space for query-time relevancy. On the LERF dataset, overall localization accuracy reaches $y(t) = (x * h)(t),$7 versus $y(t) = (x * h)(t),$8 for LERF, and IoU reaches $y(t) = (x * h)(t),$9 versus $$p_L(t) = (x * h_L)(t), \qquad p_R(t) = (x * h_R)(t).$$0; on 3D-OVS, overall mIoU reaches $$p_L(t) = (x * h_L)(t), \qquad p_R(t) = (x * h_R)(t).$$1 (Qin et al., 2023). The use of SAM-derived hierarchical masks is central to its claim of sharper object boundaries and reduced noise.

RadSplatter performs an analogous transformation for wireless propagation (Wang et al., 18 Feb 2025). Environmental scatterers and radio paths are represented by anisotropic 3D Gaussians with learned means, covariances, and beam-dependent radio attributes. A relaxed-mean reparameterization selects scatterer positions from noisy LiDAR point clouds, and a camera-free projection maps 3D Gaussians into beamspace. The predicted beamwise RSS is accumulated through electromagnetic splatting. On the synthetic Shanghai dataset, RadSplatter reports MAE $$p_L(t) = (x * h_L)(t), \qquad p_R(t) = (x * h_R)(t).$$2 and inference time $$p_L(t) = (x * h_L)(t), \qquad p_R(t) = (x * h_R)(t).$$3, outperforming NeRF$$p_L(t) = (x * h_L)(t), \qquad p_R(t) = (x * h_R)(t).$$4, VAE, and Kriging; on the real-world Hangzhou dataset it reports MAE $$p_L(t) = (x * h_L)(t), \qquad p_R(t) = (x * h_R)(t).$$5 and inference time $$p_L(t) = (x * h_L)(t), \qquad p_R(t) = (x * h_R)(t).$$6 (Wang et al., 18 Feb 2025). The method’s stated rationale is that explicit 3D Gaussians retain physical alignment while avoiding expensive volumetric integration.

SPUR extends this representational pattern to large audio-LLMs by spatializing FOA input into rotation-aware, listener-centric features (Sakshi et al., 10 Nov 2025). The FOA encoder computes banded spatial covariance matrices, applies one-pole temporal smoothing, vectorizes them into SSCV tensors of shape $$p_L(t) = (x * h_L)(t), \qquad p_R(t) = (x * h_R)(t).$$7, processes them with Conv3D and Transformer blocks, and injects the resulting tokens into frozen LALMs via a multimodal adapter and LoRA. On SPUR-Set, Audio Flamingo 3 improves from average score $$p_L(t) = (x * h_L)(t), \qquad p_R(t) = (x * h_R)(t).$$8 to $$p_L(t) = (x * h_L)(t), \qquad p_R(t) = (x * h_R)(t).$$9, while Error Rate drops from $11.0\ \mathrm{dB}$0 to $11.0\ \mathrm{dB}$1 (Sakshi et al., 10 Nov 2025). This suggests that spatialization here is best understood as an adapter-level change in representation rather than as a new generative decoder.

The same question—where spatial information is represented—appears in multichannel speech enhancement. In COSPA, effective complex masks define an implicit beamformer, and clustering analysis shows that the GRU layer, which has simultaneous access to all channels and temporal context, carries direction-of-arrival-dependent but not source-dependent features (Briegleb et al., 2023). On DST-clean, grouping success rises from $11.0\ \mathrm{dB}$2 at the GRU input to $11.0\ \mathrm{dB}$3 at the output; on DST-1Pos it falls to $11.0\ \mathrm{dB}$4, while DST-1Spk reaches $11.0\ \mathrm{dB}$5 (Briegleb et al., 2023). The paper’s conclusion is narrow but important: neural spatiospectral filters can explicitly represent spatial structure, and the representation can be localized to specific architectural components.

5. Geospatial interpolation and spatial decision support

In geostatistics, Spatialize v1.0 names a specific software system rather than a generic operation (Egaña et al., 23 Jul 2025). The library implements ensemble spatial interpolation,

$11.0\ \mathrm{dB}$6

where weak local interpolators are trained on stochastic partitions generated by Mondrian Forests or Voronoi Forests and aggregated by mean, median, percentiles, MAP, bilateral filtering, or weighted averages. IDW is available with both MF and VF, while Kriging is currently implemented with MF. The ensemble is treated as an empirical posterior, and precision maps are computed from losses between the aggregated estimate and ensemble members. On a synthetic gridded surface, the reported minimum cross-validation error for pure IDW is approximately $11.0\ \mathrm{dB}$7, while ESI-IDW reaches approximately $11.0\ \mathrm{dB}$8, about $11.0\ \mathrm{dB}$9 lower (Egaña et al., 23 Jul 2025). The library is implemented in Python 3.x with a C++ core and is designed to reduce manual variogram modeling burden.

A related but non-software use of the term appears in GIS-based urban cooling assessment. There, spatializing urban cooling potential means mapping, at parcel or pixel scale, where particular cooling techniques are likely to deliver the greatest benefits, using indicators derived from the urban energy balance and from deployment constraints (Hendel et al., 2024). The paper highlights solar irradiance, existing material properties, and underground infrastructure as essential indicators, and formalizes technique-specific deployment through a Cooling Potential Index,

$0.98\ \mathrm{dB}$0

The analysis treats cooling techniques through mechanism classes such as high-albedo materials, shading, evaporative cooling, thermal storage, and anthropogenic heat reduction (Hendel et al., 2024). Here, spatialization does not generate a signal; it converts heterogeneous urban descriptors into an actionable decision surface.

Waste mapping for flood risk offers a similar GIS pipeline but with learned perception at the front end. The Dar es Salaam workflow combines UAV orthomosaics, 360° street-view imagery, AI detectors, a common hexagonal grid, and hydrological analytics to map municipal solid waste and derive a clogging risk index,

$0.98\ \mathrm{dB}$1

Reported F1 scores are $0.98\ \mathrm{dB}$2 for street-view classification and $0.98\ \mathrm{dB}$3 for UAV detection, and waste accumulation near riverbeds is reported to be up to three times higher than in adjacent urban areas (Knoblauch et al., 20 Apr 2026). This use of spatialization is explicitly operational: the output is a GIS-ready risk layer for targeted intervention rather than a perceptual or generative field.

6. Interactive systems, embodied pipelines, and infrastructure

Some systems use spatialization as a runtime systems problem. PaperToPlace transforms paper instructions into mixed-reality experiences through an authoring pipeline and a consumption pipeline (Chen et al., 2023). Steps are segmented, associated with key objects through a fine-tuned BERT classifier, and then placed on predeclared anchoring surfaces by minimizing a weighted cost over visibility, readability, hand-angle, and user preference. The search uses simulated annealing with $0.98\ \mathrm{dB}$4 and $0.98\ \mathrm{dB}$5. In user studies, the ML-assisted authoring mode increased SUS from $0.98\ \mathrm{dB}$6 to $0.98\ \mathrm{dB}$7 and reduced workload and authoring time; in consumption, PaperToPlace reduced per-episode time, head-path length, and angular change relative to a monolithic baseline (Chen et al., 2023). Spatialization here means anchoring symbolic instruction steps to context-aware physical locations.

Spatialyze, whose official spelling differs from “Spatialize,” uses the term in a geospatial video-analytics sense (Kittivorawong et al., 2023). Its Build–Filter–Observe DSL operates over videos, cameras, geographic constructs, and movable objects, and the execution engine injects spatial-aware optimizations such as the Road Visibility Pruner and the Exit Frame Sampler. On the reported benchmarks, execution time is reduced by up to $0.98\ \mathrm{dB}$8 while maintaining up to $0.98\ \mathrm{dB}$9 accuracy compared to unoptimized execution (Kittivorawong et al., 2023). The key point is that spatialization is embedded in the query engine and optimizer, not merely in visualization.

The same shift from raw signal to spatially indexed state appears in unsupervised wireless localization. The DRL-based method for IoT data models continuous localization as an MDP with state, action, transition, reward, and discount factor $12.4\%$0, and derives reward from near-field RSS landmarks tied to known gateway positions (Li et al., 2020). With field data from Bluetooth 5 ear tags in a pasture, the method reports RMS error $12.4\%$1 and $12.4\%$2 quantile $12.4\%$3, compared with $12.4\%$4 and $12.4\%$5 for unsupervised multilateration (Li et al., 2020). In that context, spatialization means assigning location to previously unlabeled IoT measurements.

A distinct usage appears in the FIRST-S CubeSat project, where “spatialize” explicitly means to space-qualify and integrate a LiNbO$12.4\%$6 fibered nulling interferometer into a flight-ready 3U CubeSat (Lacour et al., 2014). The mission uses a $12.4\%$7 baseline, targets visible exozodiacal light, and cites an inner working angle corresponding to $12.4\%$8 at $12.4\%$9 (Lacour et al., 2014). This is the most literal infrastructural sense of the term: not rendering or interpolation, but adaptation to the space environment.

7. Limitations and methodological tensions

The literature also makes clear that spatialization is not equivalent to full physical accuracy. SoundSpaces 2.0 uses approximate edge-based diffraction, does not explicitly model very low-frequency room modes, and can suffer ray leakage when meshes contain holes (Chen et al., 2022). RadSplatter depends on LiDAR quality, assumes a dominant base station, and may not capture extremely rich scattering or dynamic occluders (Wang et al., 18 Feb 2025). LangSplat remains scene-specific and relies on SAM quality; its hierarchy is limited to whole, part, and subpart (Qin et al., 2023). Spatialize v1.0 simplifies local Kriging by using isotropic fixed variogram models and does not yet provide GPU support (Egaña et al., 23 Jul 2025).

The generative audio literature exposes a related control–fidelity trade-off. TAS does not explicitly model HRTFs or moving sources over time (Pan et al., 1 Jun 2025). MusicHiFi exposes width control but no explicit ILD, ITD, decorrelation, or panning-law parameters (Zhu et al., 2024). AudioSpa is effective in clean single-source settings but struggles when two sources must be spatialized to different directions (Feng et al., 16 Feb 2025). SpatialV2A relies on pseudo-binaural supervision rather than real binaural recordings and assumes a single dominant sounding region per frame (Wang et al., 21 Jan 2026). SPUR is limited by FOA resolution and by potential convention mismatch across FOA channel orderings and normalizations (Sakshi et al., 10 Nov 2025).

The literature surveyed here therefore suggests that “Spatialize” is best treated as a family resemblance term rather than a single technical category. In acoustics it may denote physically grounded propagation or learned binaural generation; in representation learning it may denote the assignment of semantics or radio fields to 3D coordinates; in geostatistics and GIS it may denote interpolation, scoring, or risk mapping; in systems work it may denote context-aware placement, geospatial query execution, or payload space-qualification. What unifies these usages is not modality or architecture, but the conversion of otherwise unstructured information into a form whose primary organization is spatial.