MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstruction

• URL: http://arxiv.org/abs/2206.00665v1
• Date: Wed, 1 Jun 2022 17:58:15 GMT
• Title: MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstruction
• Authors: Zehao Yu, Songyou Peng, Michael Niemeyer, Torsten Sattler, Andreas Geiger
• Abstract summary: State-of-the-art neural implicit methods allow for high-quality reconstructions of simple scenes from many input views. This is caused primarily by the inherent ambiguity in the RGB reconstruction loss that does not provide enough constraints. Motivated by recent advances in the area of monocular geometry prediction, we explore the utility these cues provide for improving neural implicit surface reconstruction.
• Score: 72.05649682685197
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Fine-detailed Neural Indoor Scene Reconstruction using multi-level importance sampling and multi-view consistency [1.912429179274357]
  We propose a novel neural implicit surface reconstruction method, named FD-NeuS, to learn fine-detailed 3D models. Specifically, we leverage segmentation priors to guide region-based ray sampling, and use piecewise exponential functions as weights to pilot 3D points sampling. In addition, we introduce multi-view feature consistency and multi-view normal consistency as supervision and uncertainty respectively, which further improve the reconstruction of details.
  arXiv  Detail & Related papers  (2024-10-10T04:08:06Z)
• NeuSurf: On-Surface Priors for Neural Surface Reconstruction from Sparse Input Views [41.03837477483364]
  We propose a novel sparse view reconstruction framework that leverages on-surface priors to achieve highly faithful surface reconstruction. Specifically, we design several constraints on global geometry alignment and local geometry refinement for jointly optimizing coarse shapes and fine details. The experimental results with DTU and BlendedMVS datasets in two prevalent sparse settings demonstrate significant improvements over the state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-12-21T16:04:45Z)
• Neural Poisson Surface Reconstruction: Resolution-Agnostic Shape Reconstruction from Point Clouds [53.02191521770926]
  We introduce Neural Poisson Surface Reconstruction (nPSR), an architecture for shape reconstruction that addresses the challenge of recovering 3D shapes from points. nPSR exhibits two main advantages: First, it enables efficient training on low-resolution data while achieving comparable performance at high-resolution evaluation. Overall, the neural Poisson surface reconstruction not only improves upon the limitations of classical deep neural networks in shape reconstruction but also achieves superior results in terms of reconstruction quality, running time, and resolution agnosticism.
  arXiv  Detail & Related papers  (2023-08-03T13:56:07Z)
• MonoNeuralFusion: Online Monocular Neural 3D Reconstruction with Geometric Priors [41.228064348608264]
  This paper introduces a novel neural implicit scene representation with volume rendering for high-fidelity online 3D scene reconstruction from monocular videos. For fine-grained reconstruction, our key insight is to incorporate geometric priors into both the neural implicit scene representation and neural volume rendering. MonoNeuralFusion consistently generates much better complete and fine-grained reconstruction results, both quantitatively and qualitatively.
  arXiv  Detail & Related papers  (2022-09-30T00:44:26Z)
• NeurAR: Neural Uncertainty for Autonomous 3D Reconstruction [64.36535692191343]
  Implicit neural representations have shown compelling results in offline 3D reconstruction and also recently demonstrated the potential for online SLAM systems. This paper addresses two key challenges: 1) seeking a criterion to measure the quality of the candidate viewpoints for the view planning based on the new representations, and 2) learning the criterion from data that can generalize to different scenes instead of hand-crafting one. Our method demonstrates significant improvements on various metrics for the rendered image quality and the geometry quality of the reconstructed 3D models when compared with variants using TSDF or reconstruction without view planning.
  arXiv  Detail & Related papers  (2022-07-22T10:05:36Z)
• NeuRIS: Neural Reconstruction of Indoor Scenes Using Normal Priors [84.66706400428303]
  We propose a new method, named NeuRIS, for high quality reconstruction of indoor scenes. NeuRIS integrates estimated normal of indoor scenes as a prior in a neural rendering framework. Experiments show that NeuRIS significantly outperforms the state-of-the-art methods in terms of reconstruction quality.
  arXiv  Detail & Related papers  (2022-06-27T19:22:03Z)
• SparseNeuS: Fast Generalizable Neural Surface Reconstruction from Sparse views [40.7986573030214]
  We introduce SparseNeuS, a novel neural rendering based method for the task of surface reconstruction from multi-view images. SparseNeuS can generalize to new scenes and work well with sparse images (as few as 2 or 3)
  arXiv  Detail & Related papers  (2022-06-12T13:34:03Z)
• Neural 3D Reconstruction in the Wild [86.6264706256377]
  We introduce a new method that enables efficient and accurate surface reconstruction from Internet photo collections. We present a new benchmark and protocol for evaluating reconstruction performance on such in-the-wild scenes.
  arXiv  Detail & Related papers  (2022-05-25T17:59:53Z)
• NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction [88.02850205432763]
  We present a novel neural surface reconstruction method, called NeuS, for reconstructing objects and scenes with high fidelity from 2D image inputs. Existing neural surface reconstruction approaches, such as DVR and IDR, require foreground mask as supervision. We observe that the conventional volume rendering method causes inherent geometric errors for surface reconstruction. We propose a new formulation that is free of bias in the first order of approximation, thus leading to more accurate surface reconstruction even without the mask supervision.
  arXiv  Detail & Related papers  (2021-06-20T12:59:42Z)

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.