Mesh-infused Neural Acoustic Field (MiNAF)

• MiNAF is an explicit context-driven model that integrates direct geometric cues from room meshes to condition accurate RIR predictions.
• It employs ray-mesh probing to construct distance-distribution based local descriptors, enhancing performance in data-sparse scenarios.
• Empirical results show MiNAF outperforms neural and conventional methods by significantly improving metrics like T60, C50, and EDT.

Searching arXiv for the MiNAF paper and related baseline methods to ground the article with current citations. arxiv_search(query="(Si et al., 18 Sep 2025) Explicit Context-Driven Neural Acoustic Modeling for High-Fidelity RIR Generation", max_results=5, sort_by="submittedDate") Searching arXiv for the MiNAF paper. arxiv_search({"query":"(Si et al., 18 Sep 2025) Explicit Context-Driven Neural Acoustic Modeling for High-Fidelity RIR Generation","max_results":5,"sort_by":"submittedDate"}) Mesh-infused Neural Acoustic Field (MiNAF) is an explicit context-driven neural acoustic model for high-fidelity room impulse response (RIR) generation that augments a neural implicit acoustic field with direct geometric features extracted from a rough room mesh. In contrast to recent neural implicit approaches that use context such as scene images but do not effectively leverage explicit geometric information, MiNAF queries the mesh at transmitter and receiver locations, constructs distance-distribution-based local descriptors, and conditions spectrum prediction on these descriptors together with spatial, temporal, orientation, and channel information. The method is introduced in "Explicit Context-Driven Neural Acoustic Modeling for High-Fidelity RIR Generation" (Si et al., 18 Sep 2025).

1. Acoustic modeling objective and conceptual position

MiNAF addresses the problem of predicting an RIR, the signal that characterizes how sound propagates from a source to a listener within a given space. The underlying premise is that neural implicit acoustic fields benefit from environment context, but that explicit local geometry can provide a more direct conditioning signal than image-derived context alone. MiNAF therefore treats the room mesh not merely as an auxiliary asset for rendering or simulation, but as a queryable geometric substrate from which local acoustic context is extracted at the transmitter and receiver positions (Si et al., 18 Sep 2025).

The core design choice is to represent local geometry through distance distributions obtained by ray-mesh probing. This makes the conditioning signal spatially explicit and tied to immediate boundary structure. The paper’s results suggest that such local geometric descriptors better guide the network in generating accurate RIR predictions, particularly in regimes with limited training data and imperfect meshes. A plausible implication is that MiNAF occupies an intermediate position between purely signal-driven neural acoustic models and fully geometry-explicit physical simulators: it preserves the compactness and continuity of implicit neural fields while exposing boundary information in a structured form.

2. Neural representation and context fusion architecture

MiNAF represents the continuous acoustic field via two small multilayer perceptrons, one predicting the log-magnitude spectrum $\Phi_{\mathrm{Mag}}$ and one predicting the instantaneous-frequency spectrum $\Phi_{\mathrm{IF}}$ (Si et al., 18 Sep 2025). Both MLPs share the same topology: 5 fully-connected layers with hidden widths exponentially decreasing from $2048 \rightarrow 1024 \rightarrow 512$, with ReLU activations between layers, and a final output layer of dimension $257$, corresponding to one short-time Fourier transform (STFT) column.

The model encodes transmitter and receiver coordinates, $\mathbf p_{Tx}$ and $\mathbf p_{Rx}$, as well as the time index $t$, using sinusoidal positional encoding $\gamma(\cdot)$ with $L=10$ frequency bands. The encoded vectors are then each passed through a two-layer compressor MLP to yield latent vectors of size $h$. Receiver orientation is represented by 4 learnable vectors of size 64 corresponding to $\Phi_{\mathrm{IF}}$0, and the binaural channel is represented by 2 learnable vectors of size 64 for $\Phi_{\mathrm{IF}}$1.

The model inputs are the transmitter position, receiver position, mesh context at the transmitter, mesh context at the receiver, the encoded time index, the receiver orientation embedding, and the channel embedding. The time embedding is element-wise multiplied into the spatial context. The fused context has shape

$\Phi_{\mathrm{IF}}$2

which serves as the input to both spectrum-predicting MLPs. At each time step, each network outputs a 257-dimensional vector representing one STFT column. The full RIR is then reconstructed by collecting all time columns and applying inverse STFT. Phase is either integrated from the predicted instantaneous frequency or recovered through variants denoted GTP, PreP, GLim, and RanP (Si et al., 18 Sep 2025).

3. Mesh querying and explicit local geometric descriptors

The explicit geometric component of MiNAF is built by probing a rough room mesh at a query location $\Phi_{\mathrm{IF}}$3. The method casts $\Phi_{\mathrm{IF}}$4 rays sampled via Fibonacci’s lattice in uniformly distributed directions. For each ray $\Phi_{\mathrm{IF}}$5, the first intersection point with the mesh is denoted $\Phi_{\mathrm{IF}}$6, and the corresponding distance is

$\Phi_{\mathrm{IF}}$7

The paper also states the more general vertex-based form

$\Phi_{\mathrm{IF}}$8

MiNAF supplements these distances with three additional classes of statistics. First, it records the surface normals $\Phi_{\mathrm{IF}}$9 at each intersection point $2048 \rightarrow 1024 \rightarrow 512$0. Second, it computes neighborhood statistics by taking, for each ray, the mean $2048 \rightarrow 1024 \rightarrow 512$1 and standard deviation $2048 \rightarrow 1024 \rightarrow 512$2 of the distances of its $2048 \rightarrow 1024 \rightarrow 512$3 angular nearest rays. Third, it constructs a global occupancy histogram using thresholds $2048 \rightarrow 1024 \rightarrow 512$4:

$2048 \rightarrow 1024 \rightarrow 512$5

Each raw feature block is embedded into $2048 \rightarrow 1024 \rightarrow 512$6 through a two-layer MLP:

$2048 \rightarrow 1024 \rightarrow 512$7

The five resulting latent vectors are concatenated into a single context vector $2048 \rightarrow 1024 \rightarrow 512$8. This design makes the geometric conditioning explicitly local: it encodes not only radial free-space extent, but also local boundary orientation, angular smoothness, and occupancy accumulation. The ablations reported later in the paper indicate that the normal features are especially consequential, which suggests that boundary orientation is particularly informative for the learned acoustic field (Si et al., 18 Sep 2025).

4. Losses, reconstruction strategy, and optimization protocol

MiNAF is trained with a combination of a spectral reconstruction term and a reverberation-decay term. The spectrum $2048 \rightarrow 1024 \rightarrow 512$9 loss is

$257$0

where $257$1 is the log-magnitude STFT at frame $257$2. To encourage correct decay behavior in the time domain, the model also uses a Schroeder-curve loss:

$257$3

The total objective is

$257$4

with $257$5 balancing spectral fidelity and accurate reverberation decay. The paper reports that no additional regularization was found necessary (Si et al., 18 Sep 2025).

Training uses SoundSpaces (Replica) with 6 rooms, split into 80% training, 5% validation, and 15% test. Optimization is performed with Adam, initial learning rate $257$6, decay by a factor of $257$7 per epoch, batch size 256, and fixed seed 42 for reproducibility. Experiments are reported on a single NVIDIA RTX 4080. Mesh preprocessing uses either the ground-truth mesh or a reconstructed mesh denoted VGGT, without further filtering. The default geometric probing parameters are $257$8 rays per point, $257$9, and $\mathbf p_{Tx}$0.

The reconstruction procedure separates magnitude and phase-related modeling. Log-magnitude is predicted directly; phase is either derived from the instantaneous-frequency prediction or supplied or recovered via the GTP, PreP, GLim, or RanP variants. The paper explicitly notes that phase prediction remains challenging, and this is reflected in the comparative behavior of the different reconstruction variants.

5. Empirical performance relative to conventional and neural baselines

Evaluation is reported using three error metrics, all with lower values indicating better performance: T60 error (%) for 60 dB energy-decay time, C50 error (dB) for the clarity index, and EDT error (s) for early decay time over 10 dB. Baselines include traditional interpolation methods—Opus-nearest, Opus-linear, AAC-nearest, AAC-linear—and neural implicit methods—INRAS, NAF, NACF, AV-NeRF, and NeRAF (Si et al., 18 Sep 2025).

Across the six-room average, MiNAF is reported in several variants. MiNAF (GTM+GTP) attains T60 error 1.49, C50 error 0.42, and EDT error 0.0023. MiNAF (PreP) attains 2.35, 0.58, and 0.0042. MiNAF (RanP) attains 1.42, 0.41, and 0.0022. MiNAF (GLim) attains 1.59, 0.41, and 0.0022. For comparison, NeRAF is reported at 2.04, 0.39, and 0.011; AV-NeRF at 2.47, 0.57, and 0.016; NACF at 2.36, 0.50, and 0.014; NAF at 3.18, 1.06, and 0.031; and INRAS at 3.14, 0.60, and 0.019. The conventional baselines are substantially worse, with Opus-nearest at 10.10, 3.58, and 0.115, and AAC-linear at 7.88, 1.68, and 0.057.

The reported highlights are more specific than a generic claim of dominance. With ground-truth phase, MiNAF achieves a 40% T60 and 26% C50 improvement over AV-NeRF. Even with random phase, MiNAF (RanP) halves the T60 error of NAF. The strongest and most consistent advantage is in EDT, where MiNAF outperforms all baselines by up to an order of magnitude. This pattern indicates that explicit local geometry is particularly effective at capturing reverberation-decay behavior, even when phase recovery is not ideal.

A common misconception is that mesh quality must be near-perfect for explicit geometric conditioning to help. The reported experiments do not support that conclusion. MiNAF remains effective with reconstructed or noisy geometry, indicating that the method does not require a finely curated watertight mesh to deliver competitive RIR prediction (Si et al., 18 Sep 2025).

6. Ablations, robustness properties, and open limitations

The ablation studies directly probe which parts of the mesh context matter. Removing all mesh context $\mathbf p_{Tx}$1 degrades T60 by 30%–40% across the examined MiNAF variants. Among individual components, removing normals $\mathbf p_{Tx}$2 has the single largest impact on all metrics. Removing neighborhood statistics $\mathbf p_{Tx}$3 or occupancy histograms also harms performance, but less than removing normals. This hierarchy suggests that local boundary orientation carries the most acoustically relevant information among the explicitly encoded mesh cues (Si et al., 18 Sep 2025).

Temporal conditioning is also nontrivial. When raw $\mathbf p_{Tx}$4 is concatenated rather than embedded element-wise into the context, T60 worsens by approximately 30%. This indicates that the temporal representation is not merely an indexing convenience; it materially affects the quality of the learned time-frequency field.

MiNAF’s robustness claims are concentrated in three settings. First, in few-shot data regimes, MiNAF degrades more gracefully than NeRAF as training data shrinks. Trained on only 10% of RIR samples, MiNAF (GLim) still outperforms AV-NeRF trained on full data by 15% in T60 and 72% in EDT. Second, under mesh perturbation, injecting up to 30 cm Gaussian noise into mesh vertex positions only slightly degrades performance. Third, a VGGT-reconstructed mesh from 45 RGB images yields nearly identical results to the ground-truth mesh.

The method also exhibits a sensitivity to probe density. Reducing the number of rays from 1024 to 512 causes an 18% drop in T60 accuracy for MiNAF PreP, and further reduction continues to degrade both magnitude and phase prediction. This reinforces that the quality of the distance distribution depends on sufficiently dense angular sampling.

The paper identifies several limitations. MiNAF must be fine-tuned per scene and does not yet generalize zero-shot to unseen rooms. Querying 1024 rays per point incurs non-trivial preprocessing cost, although the procedure is described as highly parallelizable. Phase prediction remains challenging: PreP underperforms GTP, and Griffin–Lim struggles to recover fine time-domain details. Proposed future directions include few-shot or meta-learning extensions for adaptation to new rooms, more efficient mesh probing such as learned ray sampling or voxel acceleration, improved phase modeling through hybrid time-frequency architectures or physics-inspired layers, and incorporation of explicit material absorption coefficients or learnable boundary conditions for higher-fidelity RIRs. Together, these observations position MiNAF not as a fully solved formulation of acoustic field learning, but as a concrete demonstration that explicit local geometry can materially improve neural RIR generation under realistic data and mesh constraints (Si et al., 18 Sep 2025).