From Implicit Ambiguity to Explicit Solidity: Diagnosing Interior Geometric Degradation in Neural Radiance Fields for Dense 3D Scene Understanding

• URL: http://arxiv.org/abs/2601.21421v1
• Date: Thu, 29 Jan 2026 08:58:51 GMT
• Title: From Implicit Ambiguity to Explicit Solidity: Diagnosing Interior Geometric Degradation in Neural Radiance Fields for Dense 3D Scene Understanding
• Authors: Jiangsan Zhao, Jakob Geipel, Kryzysztof Kusnierek,
• Abstract summary: We show that Neural Radiance Fields (NeRFs) saturate at approximately 89% instance recovery in dense scenes.<n>We introduce an explicit geometric pipeline based on Sparse Voxel Rasterization (SVRaster), from SfM feature geometry.<n>Our approach preserves physical solidity and achieves a 95.8% recovery rate in dense clusters.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neural Radiance Fields (NeRFs) have emerged as a powerful paradigm for multi-view reconstruction, complementing classical photogrammetric pipelines based on Structure-from-Motion (SfM) and Multi-View Stereo (MVS). However, their reliability for quantitative 3D analysis in dense, self-occluding scenes remains poorly understood. In this study, we identify a fundamental failure mode of implicit density fields under heavy occlusion, which we term Interior Geometric Degradation (IGD). We show that transmittance-based volumetric optimization satisfies photometric supervision by reconstructing hollow or fragmented structures rather than solid interiors, leading to systematic instance undercounting. Through controlled experiments on synthetic datasets with increasing occlusion, we demonstrate that state-of-the-art mask-supervised NeRFs saturate at approximately 89% instance recovery in dense scenes, despite improved surface coherence and mask quality. To overcome this limitation, we introduce an explicit geometric pipeline based on Sparse Voxel Rasterization (SVRaster), initialized from SfM feature geometry. By projecting 2D instance masks onto an explicit voxel grid and enforcing geometric separation via recursive splitting, our approach preserves physical solidity and achieves a 95.8% recovery rate in dense clusters. A sensitivity analysis using degraded segmentation masks further shows that explicit SfM-based geometry is substantially more robust to supervision failure, recovering 43% more instances than implicit baselines. These results demonstrate that explicit geometric priors are a prerequisite for reliable quantitative analysis in highly self-occluding 3D scenes.

Related papers

• MDE-VIO: Enhancing Visual-Inertial Odometry Using Learned Depth Priors [8.2208199207543]
  We propose a novel framework that enforces affine-invariant depth consistency and pairwise ordinal constraints.<n>This approach strictly adheres to the computational limits of edge devices while robustly recovering metric scale.
  arXiv  Detail & Related papers  (2026-02-11T19:53:06Z)
• Analysis of Converged 3D Gaussian Splatting Solutions: Density Effects and Prediction Limit [26.560853188856342]
  We investigate what structure emerges in 3D Gaussian Splatting (3DGS) solutions from standard multi-view optimization.<n>Dense regions exhibit geometry-correlated parameters amenable to render-free prediction, while sparse regions show systematic failure.<n>This reveals the dual character of RORs: geometric primitives where point clouds suffice, and view synthesis primitives where multi-view constraints are essential.
  arXiv  Detail & Related papers  (2026-02-09T17:09:08Z)
• InpaintHuman: Reconstructing Occluded Humans with Multi-Scale UV Mapping and Identity-Preserving Diffusion Inpainting [64.42884719282323]
  InpaintHuman is a novel method for generating high-fidelity, complete, and animatable avatars from occluded monocular videos.<n>Our approach employs direct pixel-level supervision to ensure identity fidelity.
  arXiv  Detail & Related papers  (2026-01-05T13:26:02Z)
• Visibility-Aware Densification for 3D Gaussian Splatting in Dynamic Urban Scenes [7.253732091582086]
  VAD-GS is a 3DGS framework tailored for geometry recovery in challenging urban scenes.<n>Our method identifies unreliable geometry structures via voxel-based visibility reasoning.<n>It selects informative supporting views through diversity-aware view selection, and recovers missing structures via patch matching-based stereo reconstruction.
  arXiv  Detail & Related papers  (2025-10-10T13:22:12Z)
• HBSplat: Robust Sparse-View Gaussian Reconstruction with Hybrid-Loss Guided Depth and Bidirectional Warping [11.035994094874141]
  HBSplat is a framework that seamlessly integrates robust structural cues, virtual view constraints, and occluded region completion.<n> HBSplat sets a new state-of-the-art, achieving up to 21.13 dB PSNR and 0.189 LPIPS, while maintaining real-time inference.
  arXiv  Detail & Related papers  (2025-09-29T15:03:31Z)
• OracleGS: Grounding Generative Priors for Sparse-View Gaussian Splatting [78.70702961852119]
  OracleGS reconciles generative completeness with regressive fidelity for sparse view Gaussian Splatting.<n>Our approach conditions the powerful generative prior on multi-view geometric evidence, filtering hallucinatory artifacts while preserving plausible completions in under-constrained regions.
  arXiv  Detail & Related papers  (2025-09-27T11:19:32Z)
• Seeing 3D Through 2D Lenses: 3D Few-Shot Class-Incremental Learning via Cross-Modal Geometric Rectification [59.17489431187807]
  We propose a framework that enhances 3D geometric fidelity by leveraging CLIP's hierarchical spatial semantics.<n>Our method significantly improves 3D few-shot class-incremental learning, achieving superior geometric coherence and robustness to texture bias.
  arXiv  Detail & Related papers  (2025-09-18T13:45:08Z)
• Learning Fine-Grained Geometry for Sparse-View Splatting via Cascade Depth Loss [15.425094458647933]
  We introduce Hierarchical Depth-Guided Splatting (HDGS), a depth supervision framework that progressively refines geometry from coarse to fine levels.<n>By enforcing multi-scale depth consistency, our method substantially improves structural fidelity in sparse-view scenarios.
  arXiv  Detail & Related papers  (2025-05-28T12:16:42Z)
• Missing Data Estimation for MR Spectroscopic Imaging via Mask-Free Deep Learning Methods [0.0]
  We propose the first deep learning-based, mask-free framework for estimating missing data in MRSI metabolic maps.<n>Our model generalizes well to real-world datasets without requiring retraining or mask input.
  arXiv  Detail & Related papers  (2025-05-11T01:56:26Z)
• StableGS: A Floater-Free Framework for 3D Gaussian Splatting [9.935869165752283]
  3D Gaussian Splatting (3DGS) reconstructions are plagued by stubborn floater" artifacts that degrade their geometric and visual fidelity.<n>We propose StableGS, a novel framework that decouples geometric regularization from final appearance rendering.<n> Experiments on multiple benchmarks show StableGS not only eliminates floaters but also resolves the common blur-artifact trade-off.
  arXiv  Detail & Related papers  (2025-03-24T09:02:51Z)
• Robust Incremental Structure-from-Motion with Hybrid Features [73.55745864762703]
  We introduce an incremental Structure-from-Motion (SfM) system that leverages lines and their structured geometric relations. Our system is consistently more robust and accurate compared to the widely used point-based state of the art in SfM.
  arXiv  Detail & Related papers  (2024-09-29T22:20:32Z)
• On Robust Cross-View Consistency in Self-Supervised Monocular Depth Estimation [56.97699793236174]
  We study two kinds of robust cross-view consistency in this paper. We exploit the temporal coherence in both depth feature space and 3D voxel space for self-supervised monocular depth estimation. Experimental results on several outdoor benchmarks show that our method outperforms current state-of-the-art techniques.
  arXiv  Detail & Related papers  (2022-09-19T03:46:13Z)
• Orthogonal Matrix Retrieval with Spatial Consensus for 3D Unknown-View Tomography [58.60249163402822]
  Unknown-view tomography (UVT) reconstructs a 3D density map from its 2D projections at unknown, random orientations. The proposed OMR is more robust and performs significantly better than the previous state-of-the-art OMR approach.
  arXiv  Detail & Related papers  (2022-07-06T21:40:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.