NeRF-Optimized Signal Encoding and Vision-Based 3D Reconstruction for Real-Time Semantic Scene Understanding

Reconstructing accurate and efficient three-dimensional (3D) scenes from two-dimensional (2D) images remains a fundamental challenge in computer vision, graphics, and intelligent systems. Traditional approaches—such as stereo matching, structure-from-motion, and multi-view stereo—have contributed significantly to this field. However, these methods often require dense image inputs and struggle to capture fine structural details, especially under sparse or noisy conditions.

Recent advances in deep learning have reshaped the landscape of 3D reconstruction. Among them, Neural Radiance Fields (NeRF) have emerged as a highly effective solution. NeRF represents a scene as a continuous volumetric function, allowing for the generation of photorealistic views from limited image inputs. This deep implicit neural representation captures both geometry and appearance with impressive fidelity and flexibility.

Despite their powerful representation capabilities, conventional NeRF architectures are constrained by heavy computational demands. Specifically, they rely on dense sampling along camera rays and repeated evaluations of deep multilayer perceptrons (MLPs), leading to slow inference. Such limitations hinder their application in real-time, latency-sensitive scenarios, including autonomous navigation, robotics, and augmented reality (AR).

To address these challenges, a number of acceleration strategies have been proposed. Voxel-based encodings, adaptive hierarchical sampling, and hybrid implicit-explicit representations [1] have all shown promise in reducing computation time. However, these methods often face trade-offs between rendering quality and speed. Moreover, most fail to incorporate semantic-level scene understanding, which is crucial for intelligent systems that require both perception and interaction.

In this paper, we introduce a novel NeRF-driven framework designed specifically for real-time 3D object and scene understanding. Our approach combines fast inference, geometric precision, and semantic awareness. The main contributions of this work are:

• A hybrid implicit-explicit encoding strategy that reduces computational complexity while preserving fine structural detail.
• An optimized ray sampling mechanism that significantly accelerates rendering and inference processes.
• An integrated semantic understanding module that enables real-time object-level interaction and dynamic scene parsing.

Extensive experiments demonstrate that our method outperforms conventional NeRF models in both speed and fidelity. Furthermore, the semantic integration enhances its practicality in real-world applications, including robotics perception, augmented reality, and intelligent vision systems.

We propose a novel NeRF-based framework designed for real-time 3D reconstruction and semantic scene understanding. Our architecture addresses the limitations of conventional NeRF models by introducing three key modules:

• A hybrid implicit-explicit encoding backbone that balances accuracy and speed,
• An adaptive sampling scheme that significantly reduces computational cost,
• A semantic rendering branch that enables high-level understanding of scene structure.

An overview of the pipeline is illustrated in Figure 1.

Figure 1. Pipeline of the proposed real-time NeRF framework with hybrid encoding and semantic rendering

3.1 Hybrid implicit-explicit encoding

Traditional NeRF relies entirely on implicit multilayer perceptrons (MLPs) to regress radiance and density, requiring dense point sampling and continuous evaluation, which hinders real-time applications. To alleviate this, we propose a hybrid representation combining an explicit voxel-based structure and an implicit neural function.

We partition the scene into a sparse voxel grid $\mathcal{V} \in \mathbb{R}^{N \times N \times N}$, where each voxel stores a feature vector $f_v$ precomputed from image-space information (e.g., geometry priors or coarse color estimations). These features are used to condition the implicit network $f_\theta$, enabling localized detail without full MLP evaluations.

The neural function estimates volume density $\sigma \in \mathbb{R}$ and RGB color $c \in \mathbb{R}^3$ for a 3D location $\mathrm{x} \in \mathbb{R}^3$ as follows:

$c, \sigma=f_\theta\left(\phi(\mathbb{X}), d, f_v\right)$,

where,

• $\phi(\mathbb{X})$ denotes positional encoding of the 3D point;
• $d$ is the viewing direction;
• $f_v$ is the interpolated feature from the explicit grid.

This architecture reduces the burden of continuous MLP evaluation and allows for efficient lookup-based approximation in regions with lower detail variance.

To determine how much each branch contributes at any location, we maintain a lightweight occupancy grid that stores an exponential moving average of predicted densities observed during training. For every spatial query, an occupancy-dependent blending weight determines the contribution of the explicit voxel feature versus the implicit positional encoding. High-occupancy regions—where observations are dense and geometry is reliable—favor explicit voxel features, which excel at preserving fine local detail. Conversely, low-occupancy areas rely more on the implicit MLP, which provides smoother interpolation and better generalization in regions with limited evidence.

This adaptive hybrid encoding enables the model to automatically select the most appropriate representation for each region in the scene. Importantly, explicit-only and implicit-only variants emerge as natural boundary cases when the blending weight saturates. The revised description clarifies the initialization, update mechanism, and selection rule embedded in the hybrid scheme, strengthening both methodological transparency and reproducibility.

3.2 Optimized ray sampling strategy

Standard NeRF pipelines perform uniform sampling along each ray, which results in redundant computation in free space or textureless regions. To reduce inference time, we introduce a two-stage adaptive sampling strategy informed by geometric and semantic priors.

Stage 1: Coarse Estimation.

We first perform a coarse forward pass along each ray with $M_c$ uniformly distributed samples to estimate approximate occupancy via a lightweight density estimator $\hat{\sigma}_c$. These estimates are used to compute a probability density function (PDF) over depth values.

Stage 2: Importance Sampling.

Using the computed PDF, we resample $M_f$ fine points $\left\{\mathbb{x}_j\right\}_{j=1}^{M_f}$ along the ray, focusing on regions of high structural complexity or semantic relevance. These samples are passed through the hybrid network for final rendering.

The expected color along a ray r is computed using volumetric integration:

$\widehat{\mathrm{C}}(r)=\sum_{j=1}^{M_f} T_j \alpha_j c_j, T_j=\exp \left(-\sum_{k=1}^{j-1} \sigma_k \delta_k\right), \alpha_j=1-\exp \left(-\sigma_j \delta_j\right)$

where, $\delta_j$ is the distance between adjacent samples.

Semantic-Aware Importance Sampling

To integrate semantic relevance into the sampling process, we use the semantic logits predicted during the coarse stage. For each coarse point $x_i$, the semantic logits are converted into a relevance score via softmax normalization and combined with the coarse density to modulate the sampling probability:

• regions with high predicted density and high semantic response receive more fine-stage samples
• background, textureless, or semantically uninformative regions receive fewer samples

Operationally, the coarse-stage sampling PDF is modified by a multiplicative semantic term:

$w_i=\sigma_i \cdot \operatorname{softmax}\left(s_i\right)$

where, $\sigma_i$ is the coarse density and softmax $\left(s_i\right)$ is the normalized semantic relevance. The resulting weights $w_i$ are renormalized to form the importance sampling PDF used in the fine-stage sampling.

This design allocates more sampling capacity to visually and semantically meaningful regions such as object boundaries and articulated structures, improving rendering fidelity and semantic accuracy while keeping the total number of sampled points unchanged.

3.3 Semantic-aware rendering head

To support real-time object-level interaction and scene parsing, we embed semantic reasoning directly into the rendering pipeline. We extend the output of the hybrid decoder to jointly estimate semantic logits $s \in \mathbb{R}^K$, where $K$ is the number of semantic classes.

The final decoder outputs:

$c, \sigma, s=f_\theta\left(\phi(X), d, f_v\right)$

and the semantic map $s \in \mathbb{R}^{W \times H \times K}$ is rendered through weighted accumulation analogous to the radiance map:

$\hat{S}(r)=\sum_{j=1}^{M_f} T_j \alpha_j c_j$

where, $T_j$ and $\alpha_j$ are standard NeRF transmittance and opacity terms, respectively. This formulation fuses semantic information with geometry and appearance in a unified renderable representation, supporting downstream tasks such as semantic segmentation, instance parsing, and object tracking.

Although the semantic field is represented volumetrically, supervision is applied purely in 2D image space. For each training pixel, the rendered semantic distribution $\hat{S}(r)$ is compared with the ground-truth pixel label $y(r)$ using cross-entropy loss:

$L_{\mathrm{sem}}=\operatorname{CE}(\hat{S}(r), y(r))$

Thus, no 3D semantic annotations are required. The 3D semantic field is implicitly learned through differentiable volumetric rendering, which naturally aligns volumetric predictions with image-space labels. This approach follows the established “semantic NeRF” paradigm and provides a coherent mechanism to fuse geometry, appearance, and semantics within a single neural rendering framework.

3.4 Training objective

Our model is trained end-to-end with a composite loss function:

$\mathcal{L}_{\text {total }}=\lambda_{r g b} \mathcal{L}_{r g b}+\lambda_{\text {sem}} \mathcal{L}_{\text {sem}}+\lambda_{\text {reg}} \mathcal{L}_{\text {reg }}$

where,

• $\mathcal{L}_{r g b}$ is the L2 loss between predicted and ground-truth pixel colors;
• $\mathcal{L}_{\text {sem}}$ is the cross-entropy loss for semantic labels;
• $\mathcal{L}_{\text {reg}}$ includes sparsity or entropy-based regularization to improve robustness and generalization.

To improve stability and robustness, the regularization term $L_{\text {reg}}$ incorporates both sparsity and entropy-based components:

$L_{\mathrm{reg}}=\lambda_{\mathrm{sparse}} L_{\mathrm{sparse}}+\lambda_{\mathrm{ent}} L_{\mathrm{ent}}$

The sparsity term encourages compact occupancy and suppresses floating artifacts by penalizing unnecessarily large density responses along each ray:

$L_{\text {sparse }}=\frac{1}{N_r} \sum_r \sum_j\left|\sigma_j(r)\right|$

where, $N_r$ is the number of rays and $\sigma_j(r)$ is the predicted density at the $j$-th sample along ray $r$.

The entropy term follows the entropy-maximization principle of EM-GAN and mitigates overconfident predictions in ambiguous or weakly supervised regions. It is defined as:

$L_{\mathrm{ent}}=-\frac{1}{N_r} \sum_r H(\hat{S}(r))$

where, $H(\cdot)$ denotes Shannon entropy and $\hat{S}(r)$ is the rendered per-pixel semantic distribution. Because the overall objective is minimized, the negative sign promotes higher entropy when appropriate, preventing premature semantic collapse and improving generalization under noisy or shifted domains.

Overall, the sparsity and entropy-based regularizers work complementarily: the former stabilizes the volumetric field by reducing spurious geometry, while the latter prevents the semantic branch from overfitting to uncertain regions. As demonstrated in Section 4.4, these mechanisms improve robustness to noise, imperfect labels, and domain shift.

3.5 Algorithmic implementation overview

To facilitate reproducibility and highlight the key operational steps of our framework, we present the complete inference procedure in Algorithm 1. It summarizes the hybrid feature fusion, hierarchical sampling, and semantic-aware rendering pipeline introduced in Sections 3.1-3.3.

In practice, achieving real-time inference with high-fidelity rendering requires not only algorithmic efficiency but also careful implementation optimization. To this end, we incorporate several engineering-level techniques into our framework.

First, to reduce memory overhead, we leverage shared latent embeddings across adjacent voxel cells, allowing feature reuse without repeated computation. Second, our ray sampling module is implemented using CUDA-accelerated batch ray marching to fully exploit GPU parallelism. Furthermore, we cache the coarse sampling results and reuse them during training to avoid redundant computations across epochs.

To enhance training convergence and generalization, we adopt a coarse-to-fine learning schedule where the voxel grid resolution is gradually increased. This ensures early-stage global structure alignment and late-stage fine detail refinement. Finally, all modules are implemented using TensorRT-compatible layers to facilitate real-time deployment on embedded platforms.

Algorithm 1. Real-time hybrid NeRF inference with semantic rendering

The experiments presented in this study demonstrate that the proposed NeRF-driven framework effectively unifies high-fidelity 3D reconstruction, semantic scene parsing, and real-time efficiency. By leveraging a hybrid implicit–explicit encoding strategy and an optimized sampling design, the system achieves a favorable trade-off between rendering accuracy and computational cost. Unlike classical NeRF models that emphasize quality at the expense of speed, or acceleration-oriented variants that sacrifice detail, our method delivers both visual fidelity and scalability, thereby narrowing the gap between research prototypes and real-world deployment.

5.1 Practical implications

The integration of semantic understanding into a real-time NeRF framework extends its applicability across several domains (Table 5):

• Robotics and Navigation: The framework equips autonomous agents with both geometric precision and semantic awareness, improving tasks such as obstacle avoidance, object manipulation, and safe trajectory planning.
• Augmented and Mixed Reality: Low-latency, context-aware rendering supports immersive AR/MR experiences, enabling environment-aware visualization and interactive scene adaptation.
• Medical Imaging: Extending the framework to volumetric modalities (e.g., CT, MRI) could enhance anatomical reconstruction, while semantic cues aid in tissue delineation and pathology identification.
• Cultural Heritage and Digital Twins: Efficient and semantically enriched reconstructions provide scalable solutions for artifact preservation, virtual restoration, and industrial facility modeling.
• Edge and Embedded Deployment: The reduced parameter footprint and memory efficiency make the framework viable for embedded hardware, broadening its potential for mobile and portable systems.

Table 5. Summary of discussion: contributions, applications, limitations, and future directions of the proposed framework

Together, these implications highlight the framework’s potential as not only a theoretical contribution but also a deployable tool for next-generation 3D perception systems.

5.2 Limitations and future directions

Despite its advantages, the framework presents several limitations that define avenues for further study:

• Dynamic Scenes: Performance may degrade in highly dynamic environments with extreme illumination changes or rapid motion. Incorporating temporal consistency and motion-aware sampling could address this challenge.
• Training Efficiency: Although inference is accelerated, training remains resource-intensive. Techniques such as knowledge distillation, progressive learning schedules, or neural architecture search may improve scalability.
• Semantic Supervision: The reliance on annotated datasets constrains applicability in domains with scarce labels. Future research should explore weakly supervised or self-supervised strategies.
• Deployment Constraints: While memory usage is reduced, ultra-constrained edge devices still pose challenges. Model compression, quantization, and pruning could further lower the computational barrier.
• Generative Enhancements: Integrating adversarial or diffusion priors may improve realism and generalization, as well as enable controllable scene synthesis.

Overall, these limitations highlight directions for advancing robust, efficient, and generalizable neural rendering pipelines suitable for diverse real-world contexts.