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VCR-GauS: View Consistent Depth-Normal Regularizer for Gaussian Surface Reconstruction
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Although 3D Gaussian Splatting has been widely studied because of its realistic and efficient novel-view synthesis, it is still challenging to extract a high-quality surface from the point-based representation. Previous works improve the surface by incorporating geometric priors from the off-the-shelf normal estimator. However, there are two main limitations: 1) Supervising normals rendered from 3D Gaussians effectively updates the rotation parameter but is less effective for other geometric parameters; 2) The inconsistency of predicted normal maps across multiple views may lead to severe reconstruction artifacts. In this paper, we propose a Depth-Normal regularizer that directly couples normal with other geometric parameters, leading to full updates of the geometric parameters from normal regularization. We further propose a confidence term to mitigate inconsistencies of normal predictions across multiple views. Moreover, we also introduce a densification and splitting strategy to regularize the size and distribution of 3D Gaussians for more accurate surface modeling. Compared with Gaussian-based baselines, experiments show that our approach obtains better reconstruction quality and maintains competitive appearance quality at faster training speed and 100+ FPS rendering.
Forward citations
Cited by 8 Pith papers
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ToF-Splatting: Dense SLAM using Sparse Time-of-Flight Depth and Multi-Frame Integration
ToF-Splatting is the first 3D Gaussian Splatting SLAM system designed for sparse ToF depth, using a retrained multi-frame depth integration module with monocular cues and outlier filtering.
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GauSTAR: Gaussian Surface Tracking and Reconstruction
A Gaussian-on-mesh representation with adaptive unbinding and re-meshing achieves best-on-reported-sequences dynamic surface reconstruction, rendering, and tracking under topology changes.
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ChatSplat: 3D Conversational Gaussian Splatting
ChatSplat learns a 3D conversational field in Gaussian Splatting that supports object-, view-, and scene-level chat with an LLM at real-time speeds.
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AGS-Mesh: Adaptive Gaussian Splatting and Meshing with Geometric Priors for Indoor Room Reconstruction Using Smartphones
Adaptive filtering of noisy phone depth and monocular normal priors improves Gaussian splatting based 3D indoor reconstruction and mesh extraction.
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GSurf: Learning Signed Distance Fields from Splatting Opaque Gaussians for High-quality 3D Reconstruction
GSurf learns a signed distance field supervised by Gaussian splat centers and renders via splatting, yielding compact meshes faster than previous Gaussian-SDF hybrids.
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GS-2DGS: Geometrically Supervised 2DGS for Reflective Object Reconstruction
A 2D Gaussian Splatting method that uses foundation-model depth/normal priors plus deferred shading to improve reconstruction and relighting of reflective objects.
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FatesGS: Fast and Accurate Sparse-View Surface Reconstruction using Gaussian Splatting with Depth-Feature Consistency
FatesGS combines local monocular depth ranking, depth smoothing, and multi-view feature alignment in a 2D Gaussian splatting pipeline to obtain accurate surface meshes from only three views without dataset-scale pre-training.
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GLS: Geometry-aware 3D Language Gaussian Splatting
GLS jointly optimizes 3D Gaussian surface reconstruction and open-vocabulary segmentation, reporting improved accuracy on MuSHRoom, ScanNet++ and LERF-OVS.
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