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DN-Splatter: Depth and Normal Priors for Gaussian Splatting and Meshing

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arxiv 2403.17822 v3 pith:KLAIWYRG submitted 2024-03-26 cs.CV

Matias Turkulainen , Xuqian Ren , Iaroslav Melekhov , Otto Seiskari , Esa Rahtu , Juho Kannala This is my paper · ORCID
classification cs.CV
keywords depthgaussianindoorscenessplattingnoveloptimizationchallenging
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High-fidelity 3D reconstruction of common indoor scenes is crucial for VR and AR applications. 3D Gaussian splatting, a novel differentiable rendering technique, has achieved state-of-the-art novel view synthesis results with high rendering speeds and relatively low training times. However, its performance on scenes commonly seen in indoor datasets is poor due to the lack of geometric constraints during optimization. In this work, we explore the use of readily accessible geometric cues to enhance Gaussian splatting optimization in challenging, ill-posed, and textureless scenes. We extend 3D Gaussian splatting with depth and normal cues to tackle challenging indoor datasets and showcase techniques for efficient mesh extraction. Specifically, we regularize the optimization procedure with depth information, enforce local smoothness of nearby Gaussians, and use off-the-shelf monocular networks to achieve better alignment with the true scene geometry. We propose an adaptive depth loss based on the gradient of color images, improving depth estimation and novel view synthesis results over various baselines. Our simple yet effective regularization technique enables direct mesh extraction from the Gaussian representation, yielding more physically accurate reconstructions of indoor scenes.

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Cited by 8 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. PanoLess: Environment Reconstruction from Partial Reflective Views

    cs.CV 2026-07 conditional novelty 6.0 of 10

    PanoLess recovers a distant illumination cubemap from partial reflective views using surface-aligned Gaussian splats, with a visibility map marking unsupported directions.

  2. GS-Occ3D: Scaling Vision-only Occupancy Reconstruction with Gaussian Splatting

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A camera-only Gaussian-surfel pipeline reconstructs full Waymo scenes, converts them to binary occupancy labels, and trains CVT-Occ to generalize on Occ3D-Waymo and Occ3D-nuScenes at a level close to or above LiDAR-la...

  3. Flow Distillation Sampling: Regularizing 3D Gaussians with Pre-trained Matching Priors

    cs.CV 2025-02 conditional novelty 6.0 of 10

    A new loss that matches an optical-flow model's predictions against analytically computed flows from 3D Gaussians, improving geometric reconstruction on sparse indoor scenes.

  4. Leveraging 2D Priors and SDF Guidance for Dynamic Urban Scene Rendering

    cs.CV 2025-10 conditional novelty 5.0 of 10

    UGSDF achieves state-of-the-art novel-view rendering of dynamic urban objects without LiDAR or 3D motion annotations by jointly optimizing SDFs and 3D Gaussians under 2D depth and point-tracking priors.

  5. Doctoral Thesis: Geometric Deep Learning For Camera Pose Prediction, Registration, Depth Estimation, and 3D Reconstruction

    cs.CV 2025-09 conditional novelty 5.0 of 10

    A PhD thesis showing that adding geometric priors (skyline, normals, focus cues, wavelet depth) to deep networks improves pose estimation, registration, depth prediction, and reconstruction.

  6. Unveiling Trust in Multimodal Large Language Models: Evaluation, Analysis, and Mitigation

    cs.CL 2025-08 unverdicted novelty 5.0 of 10

    MultiTrust-X is a new 32-task, 28-dataset benchmark over 30 multimodal LLMs claiming that trustworthiness lags capability, that multimodality amplifies base-model risks, and that its RESA alignment method reaches stat...

  7. SuperGS: Consistent and Detailed 3D Super-Resolution Scene Reconstruction via Gaussian Splatting

    cs.CV 2025-05 conditional novelty 5.0 of 10

    SuperGS outperforms prior Gaussian-splatting methods on high-resolution novel view synthesis by combining a latent feature field, multi-view voting densification, and variational uncertainty weighting.

  8. GS4Buildings: Prior-Guided Gaussian Splatting for 3D Building Reconstruction

    cs.CV 2025-08 conditional novelty 4.0 of 10

    GS4Buildings uses LoD2 building models to initialize and supervise 2D Gaussian Splatting, reporting better urban reconstruction metrics, though completeness evaluation against LoD3-derived references is partially confounded.

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