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MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstruction

Zehao Yu, Songyou Peng, Michael Niemeyer, Torsten Sattler, Andreas Geiger

arXiv:2206.00665v2cs.CV

TL;DR

Neural implicit reconstruction is underconstrained by RGB-only losses in sparse-view, textureless, and complex scenes. MonoSDF adds monocular depth and normal cues while comparing multiple implicit representations, finding improved quality and efficiency across architectures and scene scales.

  • Problem

    RGB reconstruction losses leave neural implicit surface reconstruction underconstrained, especially in less-observed and textureless regions of complex or sparsely viewed scenes.

  • Method

    MonoSDF guides neural implicit surface optimization with monocular depth and normal predictions while systematically comparing MLP- and grid-based representations.

  • Results

    Monocular geometric priors significantly improve reconstruction quality, efficiency, and scalability across neural implicit representations and scene settings.

  • Takeaways & Limitations

    Geometric monocular priors provide a general drop-in improvement for neural implicit reconstruction, independent of the underlying representation.

Abstract

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In recent years, neural implicit surface reconstruction methods have become popular for multi-view 3D reconstruction. In contrast to traditional multi-view stereo methods, these approaches tend to produce smoother and more complete reconstructions due to the inductive smoothness bias of neural networks. State-of-the-art neural implicit methods allow for high-quality reconstructions of simple scenes from many input views. Yet, their performance drops significantly for larger and more complex scenes and scenes captured from sparse viewpoints. This is caused primarily by the inherent ambiguity in the RGB reconstruction loss that does not provide enough constraints, in particular in less-observed and textureless areas. Motivated by recent advances in the area of monocular geometry prediction, we systematically explore the utility these cues provide for improving neural implicit surface reconstruction. We demonstrate that depth and normal cues, predicted by general-purpose monocular estimators, significantly improve reconstruction quality and optimization time. Further, we analyse and investigate multiple design choices for representing neural implicit surfaces, ranging from monolithic MLP models over single-grid to multi-resolution grid representations. We observe that geometric monocular priors improve performance both for small-scale single-object as well as large-scale multi-object scenes, independent of the choice of representation.

1 Introduction

MonoSDF addresses the difficulty of reconstructing complex, large, sparsely viewed, and textureless scenes by adding monocular geometric cues to neural implicit surface optimization. It also systematically compares neural implicit representations and finds benefits across architectures.

  • Motivation: Neural implicit methods struggle with limited views, complex multi-object scenes, and large textureless regions because RGB reconstruction is underconstrained.RGB-only optimization admits infinitely many photo-consistent explanations.
  • Approach: MonoSDF uses depth and surface-normal predictions from general-purpose monocular networks as additional supervision alongside RGB reconstruction.The monocular cues are used during optimization of neural implicit surface models.
  • Geometric Priors: Monocular geometric priors improve reconstruction quality in textureless and less-observed areas by complementing photometric consistency cues.The paper attributes this complementarity to reliable normal prediction where photometric consistency fails.
  • Representation Analysis: The study compares single MLP, dense SDF grid, single feature grid, and multi-resolution feature-grid representations.These choices span monolithic, explicit-grid, and hybrid neural implicit architectures.
  • Results: Monocular priors improve neural implicit reconstruction across representations, with faster convergence and benefits for object-, room-, and large-scale scene settings.Experiments cover DTU, Replica, ScanNet, and Tanks and Temples.

2 Related Work

Neural implicit scene representations use compact neural fields for geometry and appearance, while prior reconstruction work combines surface and volume rendering. MonoSDF builds on these directions by studying architectures and monocular geometric guidance.

  • Neural Implicit Representations: Neural implicit representations use neural fields, often combining MLP decoders with voxel-grid features to represent geometry and appearance.Hybrid grids improve detail and evaluation speed but increase memory use with scene size.
  • Neural Implicit Representations: Earlier neural implicit methods achieve strong object-level reconstruction but do not scale well to complicated or large-scale scenes.The passage attributes this limitation to restricted model capacity.
  • MonoSDF: MonoSDF investigates monocular geometric cues as efficient priors for guiding optimization of neural implicit surface models.The framework renders RGB, depth, and normals for ray batches and optimizes against images and geometric cues.

3 Method

MonoSDF represents scene geometry with neural implicit surfaces and optimizes them through differentiable volume rendering, RGB reconstruction, regularization, and monocular depth and normal cues. It evaluates dense, MLP, single-resolution, and multi-resolution representations, with multi-resolution feature grids producing the best architectural results.

  • Scene representations: MonoSDF represents scene geometry as a signed distance function parameterized by dense grids, MLPs, or hybrid MLP-feature-grid architectures.The investigated hybrid designs include single- and multi-resolution feature grids.
  • Volume rendering: Differentiable volume rendering casts camera rays, samples points, predicts SDF and color values, and computes rendered color, depth, and normals.The rendered quantities connect the 3D representation to image-based supervision.
  • Monocular geometric cues: Monocular depth provides relative semi-local information, while monocular normals provide local geometric detail; both are predicted with a pretrained Omnidata model.These cues are used as additional supervision alongside RGB reconstruction.
  • Optimization: The optimization combines RGB reconstruction with Eikonal, depth-consistency, and normal-consistency losses.Monocular depth is aligned per batch because it is defined only up to scale and shift, while normals are transformed into the same coordinate system.
  • Architectural ablation: Multi-resolution feature grids achieve the best architectural results, whereas dense SDF grids produce noisy reconstructions and MLP-based designs can lose geometric detail through over-smoothing.The reported explanation for dense-grid noise is the absence of a smoothness bias.

4 Experiments

Experiments evaluate architectural choices and monocular-cue ablations on Replica, then test MonoSDF on large-scale scenes and sparse or dense object-level reconstruction. Monocular cues improve reconstruction across representations, while architecture-dependent trade-offs remain in convergence speed, detail, and robustness.

  • Experimental setup: The experiments compare four scene representations on Replica and ablate monocular cues for reconstruction quality and convergence time.They then evaluate large-scale indoor scenes and DTU object reconstruction with sparse and dense input views.
  • Architecture Choices for Scene Representations: A single MLP is smoother than dense-grid representations, while latent feature grids improve over dense SDF grids and can capture more detail.Dense SDF grids lack a smoothness bias because grid-cell SDF values are optimized independently.
  • Ablation of Different Cues: Using both depth and normal cues gives the best reconstruction quality, and the cues improve results independently of the underlying scene representation.The cues are complementary, and differences between representations become negligible when they are used.
  • Optimization Time: Monocular cues significantly speed convergence, while Multi-Res. Grids converge faster and the MLP with both cues achieves the best results.After 10K iterations, both cue-augmented representations outperform their converged counterparts without cues.
  • Real-world Large-scale Scene Reconstruction: On ScanNet and Tanks & Temples, MonoSDF produces smooth, detailed large-scale reconstructions and achieves the best reported results in the cited comparisons.On Tanks & Temples, monocular cues boost VolSDF enough to obtain reasonable results on a large-scale indoor scene.
  • Object-level Reconstruction: With three DTU views, monocular cues significantly improve both MLP and Multi-Res. Grid reconstructions, while the single MLP performs better than grid representations.With all DTU input views, the MLP is similar to prior methods, whereas Multi-Res. Grids outperform prior work by a large margin.

5 Conclusion

MonoSDF integrates monocular geometric cues into neural implicit surface reconstruction and studies how they interact with scene representations. The cues improve reconstruction quality, efficiency, and scalability across settings, with representation-specific trade-offs and predictor-quality dependence.

  • MonoSDF integrates monocular geometric cues into neural implicit surface optimization and systematically compares scene representations.The framework uses depth and normal cues alongside RGB reconstruction supervision.
  • Monocular cues improve reconstruction quality, efficiency, and scalability across neural implicit representations.The conclusion reports benefits for a variety of representations and scene settings.
  • A simple MLP performs best overall with cues but converges more slowly, whereas Multi-Res. Grids converge quickly and capture detail while remaining less robust to noise and ambiguity.
  • Limitations: Performance depends on the quality of monocular cues, motivating filtering strategies for failures of the monocular predictor.

Checklist

The checklist records affirmative disclosures for claims, limitations, societal impacts, reproducibility, training details, compute resources, and asset citation. It also notes that error bars were not reported.

  • The authors indicate that the main claims accurately reflect the paper’s contributions and scope and that limitations are discussed.
  • Potential negative societal impacts are discussed in the supplementary material.
  • The experiments report code and data release, training details, and computational-resource information.
  • Error bars were not reported for repeated experiments.
  • The supplementary material covers architectural details, additional cue ablations, further results, and potential negative impact.

A.4 Evaluation Metrics

The paper evaluates reconstruction using dataset-specific geometric and consistency metrics. DTU uses Accuracy, Completeness, and Chamfer Distance; Replica and ScanNet add precision, recall, F-score, and, for Replica, normal consistency, while Tanks & Temples reports F-score.

  • DTU evaluation reports Accuracy, Completeness, and Chamfer Distance.Accuracy measures reconstructed-to-ground-truth distance, while Completeness measures ground-truth-to-reconstructed distance.
  • Replica and ScanNet evaluation reports Accuracy, Completeness, Chamfer Distance, Precision, Recall, and F-score at a 5cm threshold.
  • Replica additionally reports Normal Consistency because near-perfect ground truth is available.
  • Tanks & Temples evaluation reports the F-score provided by its official evaluation server.

B Ablation

The ablations evaluate monocular geometric cues across architectures and input-view counts, finding consistent reconstruction improvements, including under sparse-view conditions.

  • The ablation studies cover geometric cues with different scene representations, architecture configurations, input-view counts, cue predictors, and optimization time.
  • Monocular geometric cues significantly improve reconstruction quality across all evaluated architectures.
  • Using more input views improves reconstruction quality, while geometric cues provide additional gains.

B.1 Ablation of Different Cues

Monocular geometric cues improve reconstruction across scene representations and architecture configurations, with depth and normals providing complementary benefits and faster convergence.

  • B.1 Ablation of Different Cues: Geometric cues significantly improve reconstruction quality independent of the underlying scene representation.
  • B.1 Ablation of Different Cues: Using both depth and normal cues produces the best results, indicating complementary geometric information.
  • B.2 Ablation of Different Architecture Configurations: Geometric cues improve reconstruction quality and convergence speed independent of the network configuration.
  • B.2 Ablation of Different Architecture Configurations: The architecture study varies MLP depth and multi-resolution feature-grid hash-table size to evaluate different model capacities.

B.3 Ablation of Different Numbers of Input Views

The experiments examine input-view counts, cue predictors, optimization overhead, and dataset-level reconstruction results. Monocular cues consistently improve quality across sparse and challenging settings while adding limited computational cost.

  • B.3 Ablation of Different Numbers of Input Views: Adding monocular geometric cues consistently improves reconstruction across different numbers of input views.
  • B.4 Ablation of Different Monocular Cues Predictors: The Omnidata predictor gives the best results among tested depth and normal predictors, while better geometric cues further improve performance.
  • B.5 Optimization Time: 36 FPS cue prediction and less than 26 seconds for 464 ScanNet images indicate a small preprocessing overhead.
  • C.1 ScanNet: The MonoSDF MLP produces better ScanNet reconstructions than state-of-the-art methods, visually and quantitatively.
  • C.2 Tanks and Temples: Monocular cues significantly improve reconstruction quality on Tanks and Temples across all evaluated scenes.
  • C.4 DTU: Compared with fused MVS point clouds, MonoSDF reconstructions are more complete.

C.3 Preliminary Results of Using High-resolution Monocular Cues

The preliminary experiment explores higher-resolution monocular cues and reports finer details and significant reconstruction improvements compared with lower-resolution cues.

  • C.3 Preliminary Results of Using High-resolution Monocular Cues: Training uses high-resolution monocular cues after generating depth and normal maps beyond the main paper’s 384 × 384 resolution.
  • C.3 Preliminary Results of Using High-resolution Monocular Cues: High-resolution cues contain more fine details than low-resolution cues and significantly improve reconstruction quality.

C.4 DTU

On DTU, monocular geometric cues improve reconstruction and novel-view synthesis under sparse and full-view settings, across MLP and multi-resolution grid representations, while weight annealing helps stabilize cue usage.

  • Geometry: Monocular cues improve both MLP and Multi-Res. Grids on DTU with three input views, enabling high-quality meshes where cue-free methods fail.The paper reports this comparison qualitatively and shows a failure case separately.
  • Novel View Synthesis: Using monocular geometric cues significantly improves novel view synthesis on DTU with three input views.The comparison is reported in Table 13 and Fig. 18.
  • Weight Annealing: Exponential annealing of monocular depth and normal loss weights to zero during the first 200 epochs improves reconstruction quality.The paper attributes the schedule to the imperfection of the monocular depth and normal predictor.
  • Failure Cases: A DTU three-view failure duplicates the object in front of each camera frustum because monocular depth cues are only up to scale and lack guaranteed multi-view consistency.The paper identifies explicit multi-view constraints, such as COLMAP sparse point clouds, as a possible remedy.
  • Additional Qualitative Comparisons: On Tanks & Temples, qualitative comparisons evaluate monocular cues against no cues for single-MLP and multi-resolution-grid representations across multiple scenes and cue resolutions.The figures cover Auditorium, Ballroom, Courtroom, and Museum, including low- versus high-resolution monocular cues.
  • Geometry: With all input views, MonoSDF using multi-resolution feature grids produces more accurate reconstructions than state-of-the-art methods.The MLP variant achieves similar results to a previous method, while multi-resolution feature grids reconstruct more detailed surfaces.
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