Multi-View Inverse Rendering Framework

• The paper introduces a feed-forward multi-view inverse rendering framework that recovers scene geometry, reflectance, and lighting in a single pass without iterative optimization.
• It leverages neural architectures like CNNs and transformers to fuse calibrated multi-view images, ensuring physically consistent reconstructions and robust view-dependent effect handling.
• The framework achieves rapid, high-fidelity novel view synthesis and relighting, with applications in AR/VR, robotics, and photorealistic graphics pipelines.

A feed-forward multi-view inverse rendering framework is a class of computational models designed to recover scene geometry, spatially varying reflectance (BRDF or SVBRDF), and lighting parameters directly from a set of calibrated scene images without requiring per-scene optimization or iterative refinement. These frameworks leverage feed-forward neural architectures (e.g., CNNs, transformers, hybrid neural-graphics modules) to enable efficient, scalable, and real-time estimation of intrinsic scene properties, including robust treatment of view-dependent effects and complex material appearances. The multi-view context provides sufficient constraints to disentangle surface properties from illumination and supports coherent reconstructions across viewpoints (Wu et al., 24 Dec 2025, Li et al., 28 Apr 2025, Zhou et al., 10 Jul 2025, Choi et al., 13 Aug 2024, Choi et al., 2023, Yoshiyama et al., 2023).

1. Fundamental Principles and Problem Setting

Feed-forward multi-view inverse rendering aims at the per-pixel, per-view recovery of geometry (surface normals, depth, mesh/SDF), reflectance models (albedo, roughness, metallicity), and lighting (local and global, direct and indirect components) across multiple RGB views. The inverse rendering problem is formally under-constrained in the single-view regime but becomes tractable with well-calibrated, multi-view observations.

Mathematically, given $N$ calibrated images $S = \{I_i\}_{i=1}^N$, the system seeks spatially varying map outputs such as $A(p)$ (albedo), $M(p)$ (metallic), $R(p)$ (roughness), $N(p)$ (normal), and potentially $L(x)$ (lighting parameters), satisfying the image formation equation under a physically-based reflectance model:

$$I_i(p) = \int_{\Omega^+} f(\omega_i, \omega_o(p); \theta(p))\, L(\omega_i)\, \max\{0, n(p)\cdot\omega_i\}\, d\omega_i$$

Here, $\theta(p)$ denotes the set of per-pixel BRDF parameters. Feed-forward frameworks solve for these components as direct outputs of the model, in a single forward pass per scene (or per set of frames), without scene-specific gradient-based optimization (Wu et al., 24 Dec 2025, Zhou et al., 10 Jul 2025).

2. Core Architectural Variants

Recent frameworks instantiate the feed-forward strategy using various neural architectures and explicit-implicit scene representations:

• Hybrid neural-graphics models (RTR-GS): 3D Gaussian splatting with both a forward (radiance-transfer) and deferred (reflection) branch, efficiently decoupling low-frequency radiance from high-frequency specular (Zhou et al., 10 Jul 2025).
• Transformer-based volumetric models (LIRM): Hexa-plane SDF for geometry/materials, combined with neural directional embeddings for view-dependent radiance, supporting progressive refinement as more views are added (Li et al., 28 Apr 2025).
• Multi-stage deep networks with attention (MAIR, MAIR++): Sequential stages estimate normals/geometry, then SVBRDFs, then 3D spatially varying lighting; advanced multi-view fusion via directional attention modules enables robust aggregation of context and material cues across views (Choi et al., 13 Aug 2024, Choi et al., 2023).
• Global–local transformer-CNN hybrids (MVInverse): Alternating intra- and inter-view self-attention propagates surface and lighting information, with CNN decoders ensuring high-frequency texture fidelity. Fine-tuning on unlabeled video sequences with temporal consistency is leveraged to improve generalization (Wu et al., 24 Dec 2025).
• Volume rendering with physically-based differentiable models (NDJIR): Direct evaluation of the PBR integral by combining SDF-based geometry, Monte Carlo sampling for lighting, and per-point neural material/light heads (Yoshiyama et al., 2023).
• Feed-forward volumetric scattering inversion (TensoIS): Low-rank tensor representations of scattering coefficients are regressed from multi-view imagery for inverse subsurface scattering, using decoupled CNN encoders per view (Tiwari et al., 4 Sep 2025).

A tabular summary of selected contemporary frameworks:

Model	Geometric Representation	Material Model	Lighting Model	Multi-View Fusion
RTR-GS (Zhou et al., 10 Jul 2025)	3D Gaussians Splatting	Disney BRDF, per-Gaussian	Spherical Harmonics, voxel-baked	Splatting across rays, screen-space raster
LIRM (Li et al., 28 Apr 2025)	Hexa-plane neural SDF	SDF-conditioned neural MLPs	Neural Directional Embedding	Transformer attention, progressive update
MAIR++ (Choi et al., 13 Aug 2024)	2D/3D grids, MVS-derived	SVBRDF via MLPs/U-Nets	Implicit Lighting Representation	Mean-variance + Directional Attention
NDJIR (Yoshiyama et al., 2023)	Volumetric SDF	Neural Filament BRDF	Env map + point/implicit lights	Volume ray tracing, per-view SDF

3. Feed-Forward Training and Inference Pipeline

A canonical pipeline comprises the following:

1. Input encoding: Each image (and, if available, geometry proxy such as MVS depths/confidence) is tokenized via convolutional or transformer backbones. Geometric or positional encodings (Plücker rays, patch tokens, direction embeddings) are propagated (Wu et al., 24 Dec 2025, Li et al., 28 Apr 2025, Choi et al., 2023).
2. Fusion/aggregation: Inter-view information is fused using mechanisms such as occlusion-aware weighted mean/variance (Choi et al., 2023), directional self-attention (Choi et al., 13 Aug 2024), or alternating transformer attention along and across views (Wu et al., 24 Dec 2025). This ensures physically and photometrically consistent outputs over large baselines.
3. Scene decomposition: Decoders produce per-pixel or per-volume outputs: intrinsic images (albedo, roughness, metallic), surface normals, shading, and, for full scene relighting, a 3D spatially-varying lighting volume.
4. Hybrid rendering operations: Forward models may employ screen-space rasterization (Gaussian splatting), volume rendering (ray marching over SDFs), or compositional microfacet BRDF evaluation. Lighting terms are evaluated as Spherical Harmonic expansions, implicit neural codes, or composited spherical Gaussians (Zhou et al., 10 Jul 2025, Choi et al., 13 Aug 2024, Choi et al., 2023, Yoshiyama et al., 2023).
5. Losses and regularization: Training uses a combination of photometric, perceptual (SSIM/LPIPS), reconstruction, surface normal (cosine/angular), smoothness, and, where available, consistency losses across views/timesteps. Regularization ensures stable decompositions of material and lighting (Zhou et al., 10 Jul 2025, Wu et al., 24 Dec 2025, Choi et al., 13 Aug 2024).

4. Material, Geometry, and Lighting Decomposition

Decomposition strategies vary in granularity and explicitness:

• BRDF/SVBRDF Estimation: Most frameworks adopt parametric microfacet BRDFs (Disney, Filament/Cook–Torrance), producing per-pixel diffuse albedo, roughness, and metallicity (Zhou et al., 10 Jul 2025, Wu et al., 24 Dec 2025, Choi et al., 13 Aug 2024). For specular reflectance, both explicit (split-sum/Spherical Gaussians) and learned (neural panorama or implicit code) methods are employed.
• Geometry: Surfaces are either represented explicitly (3D Gaussian clouds, SDFs, mesh extractions via Marching Cubes), volumetrically (dense or sparse 3D grids), or as 2.5D normal/depth maps optimized per frame through normal consistency regularization (Li et al., 28 Apr 2025, Yoshiyama et al., 2023).
• Lighting: Lighting is disentangled as global components (e.g., spherical harmonics, environment maps) and local volumetric/voxel-baked terms capturing indirect illumination and shadowing. Advanced approaches introduce per-pixel implicit lighting vectors that can be decoded to both canonical (envmap) and rendering-specific (shading, specular) forms (Choi et al., 13 Aug 2024).

Regularization is essential to prevent degenerate solutions, e.g., by aligning material parameters to predicted reflectance intensity, enforcing normal consistency with proxy geometry, penalizing spatial gradients for smooth lighting, and maintaining white-balance (Zhou et al., 10 Jul 2025, Li et al., 28 Apr 2025, Choi et al., 13 Aug 2024).

5. Experimental Results and Comparative Performance

Feed-forward multi-view frameworks substantially outperform single-view or analysis-by-synthesis methods in terms of speed, multi-view consistency, and physical interpretability of outputs. Empirical results highlight:

• Novel view synthesis (object level): RTR-GS achieves PSNR ≈ 41.4/35.2/40.5 and SSIM ≈ 0.988/0.975/0.991 on TensoIR, Shiny Blender, Stanford ORB datasets, outperforming best baselines by >2 dB (Zhou et al., 10 Jul 2025). LIRM demonstrates comparable or superior view synthesis PSNR/SSIM/LPIPS versus state-of-the-art optimization methods, but with sub-second inference (Li et al., 28 Apr 2025).
• Relighting and editing: MAIR++ reduces HDR re-rendering MSE by 4–10× compared to fixed-lobe lighting models and enables material editing due to implicit lighting representations (Choi et al., 13 Aug 2024).
• Normal/material estimation: MVInverse yields multi-view albedo RMSE = 0.0494 (Hypersim, 10 views), and best single-view PSNR/SSIM/LPIPS on public datasets (Wu et al., 24 Dec 2025).
• Generalization/robustness: Consistency-based finetuning (MVInverse) and multi-light feature regularization (TensoIS) strongly increase in-the-wild performance and mitigate flicker or cross-view inconsistencies (Wu et al., 24 Dec 2025, Tiwari et al., 4 Sep 2025).
• Efficiency: Training times ∼0.5 h (RTR-GS), inference latency ∼0.3–1 s on high-end GPUs (LIRM, MAIR, MVInverse), and real-time rendering rates (∼100 FPS) are routine for modern feed-forward architectures (Zhou et al., 10 Jul 2025, Li et al., 28 Apr 2025, Choi et al., 2023).

6. Limitations and Prospects

Current feed-forward multi-view inverse rendering frameworks are limited by the need for accurate multi-view calibration and, often, synthetic/augmented training data to address the domain gap in generalization to real scenes. Some models (e.g., NDJIR, TensoIS) require moderate to dense Monte Carlo sampling or regularization to stabilize highly ill-posed decompositions under ambiguous or degenerate conditions, especially for highly specular or dark regions (Yoshiyama et al., 2023, Tiwari et al., 4 Sep 2025). Handling of volumetric scattering (TensoIS) remains challenging for colored media and very fine-scale heterogeneity.

A key research direction involves developing more robust cross-domain generalization, closing the reality gap without the need for large-scale synthetic finetuning or extensive view coverage. Incorporation of self-supervised consistency, implicit representation learning for lighting/materials, and hierarchical aggregation remains important for scalable deployment in unconstrained environments (Wu et al., 24 Dec 2025, Choi et al., 13 Aug 2024).

7. Applications and Broader Impact

Feed-forward multi-view inverse rendering pipelines underpin a wide range of practical applications: rapid capture and digitization of objects and scenes for AR/VR and autonomous robotics, photorealistic re-rendering and material editing for graphics pipelines, 3D scene understanding for vision systems, and industrial inspection by decomposing surface and illumination factors. The real-time, scene-agnostic nature of these models makes them suitable for integration into graphics engines and interactive content creation platforms (Li et al., 28 Apr 2025, Zhou et al., 10 Jul 2025).

Recent advances have established these frameworks as state-of-the-art for coherent, physically robust multi-view decomposition, laying the foundation for the next generation of inverse graphics and neural scene representations.