Robust SG-NeRF: Robust Scene Graph Aided Neural Surface Reconstruction

• URL: http://arxiv.org/abs/2411.13620v1
• Date: Wed, 20 Nov 2024 09:48:06 GMT
• Title: Robust SG-NeRF: Robust Scene Graph Aided Neural Surface Reconstruction
• Authors: Yi Gu, Dongjun Ye, Zhaorui Wang, Jiaxu Wang, Jiahang Cao, Renjing Xu,
• Abstract summary: Existing pose-NeRF joint optimization methods handle poses with small noise (inliers) effectively but struggle with large noise (outliers) In this work, we focus on mitigating the impact of outlier poses. Our method integrates an inlier-outlier confidence estimation scheme, leveraging scene graph information gathered during the data preparation phase.
• Score: 9.429618155943635
• License:
• Abstract: Neural surface reconstruction relies heavily on accurate camera poses as input. Despite utilizing advanced pose estimators like COLMAP or ARKit, camera poses can still be noisy. Existing pose-NeRF joint optimization methods handle poses with small noise (inliers) effectively but struggle with large noise (outliers), such as mirrored poses. In this work, we focus on mitigating the impact of outlier poses. Our method integrates an inlier-outlier confidence estimation scheme, leveraging scene graph information gathered during the data preparation phase. Unlike previous works directly using rendering metrics as the reference, we employ a detached color network that omits the viewing direction as input to minimize the impact caused by shape-radiance ambiguities. This enhanced confidence updating strategy effectively differentiates between inlier and outlier poses, allowing us to sample more rays from inlier poses to construct more reliable radiance fields. Additionally, we introduce a re-projection loss based on the current Signed Distance Function (SDF) and pose estimations, strengthening the constraints between matching image pairs. For outlier poses, we adopt a Monte Carlo re-localization method to find better solutions. We also devise a scene graph updating strategy to provide more accurate information throughout the training process. We validate our approach on the SG-NeRF and DTU datasets. Experimental results on various datasets demonstrate that our methods can consistently improve the reconstruction qualities and pose accuracies.

Related papers

• SG-NeRF: Neural Surface Reconstruction with Scene Graph Optimization [16.460851701725392]
  We present a novel approach that optimize radiance fields with scene graphs to mitigate the influence of outlier poses. Our method incorporates an adaptive inlier-outlier confidence estimation scheme based on scene graphs. We also introduce an effective intersection-over-union (IoU) loss to optimize the camera pose and surface geometry.
  arXiv  Detail & Related papers  (2024-07-17T15:50:17Z)
• Cameras as Rays: Pose Estimation via Ray Diffusion [54.098613859015856]
  Estimating camera poses is a fundamental task for 3D reconstruction and remains challenging given sparsely sampled views. We propose a distributed representation of camera pose that treats a camera as a bundle of rays. Our proposed methods, both regression- and diffusion-based, demonstrate state-of-the-art performance on camera pose estimation on CO3D.
  arXiv  Detail & Related papers  (2024-02-22T18:59:56Z)
• iComMa: Inverting 3D Gaussian Splatting for Camera Pose Estimation via Comparing and Matching [14.737266480464156]
  We present a method named iComMa to address the 6D camera pose estimation problem in computer vision. We propose an efficient method for accurate camera pose estimation by inverting 3D Gaussian Splatting (3DGS)
  arXiv  Detail & Related papers  (2023-12-14T15:31:33Z)
• PoRF: Pose Residual Field for Accurate Neural Surface Reconstruction [24.882446212756626]
  We introduce the pose residual field (PoRF), a novel implicit representation that uses an correspondence for regressing pose updates. On the DTU dataset, we reduce the rotation error by 78% for COLMAP poses, leading to the decreased reconstruction Chamfer distance from 3.48mm to 0.85mm. On the Mobile dataset that contains casually captured 360-degree videos, our method refines ARKit poses and improves the reconstruction F1 score from 69.18 to 75.67.
  arXiv  Detail & Related papers  (2023-10-11T12:51:16Z)
• PoseMatcher: One-shot 6D Object Pose Estimation by Deep Feature Matching [51.142988196855484]
  We propose PoseMatcher, an accurate model free one-shot object pose estimator. We create a new training pipeline for object to image matching based on a three-view system. To enable PoseMatcher to attend to distinct input modalities, an image and a pointcloud, we introduce IO-Layer.
  arXiv  Detail & Related papers  (2023-04-03T21:14:59Z)
• Enhanced Stable View Synthesis [86.69338893753886]
  We introduce an approach to enhance the novel view synthesis from images taken from a freely moving camera. The introduced approach focuses on outdoor scenes where recovering accurate geometric scaffold and camera pose is challenging.
  arXiv  Detail & Related papers  (2023-03-30T01:53:14Z)
• Progressively Optimized Local Radiance Fields for Robust View Synthesis [76.55036080270347]
  We present an algorithm for reconstructing the radiance field of a large-scale scene from a single casually captured video. For handling unknown poses, we jointly estimate the camera poses with radiance field in a progressive manner. For handling large unbounded scenes, we dynamically allocate new local radiance fields trained with frames within a temporal window.
  arXiv  Detail & Related papers  (2023-03-24T04:03:55Z)
• DFNet: Enhance Absolute Pose Regression with Direct Feature Matching [16.96571417692014]
  We introduce a camera relocalization pipeline that combines absolute pose regression (APR) and direct feature matching. We show that our method achieves a state-of-the-art accuracy by outperforming existing single-image APR methods by as much as 56%, comparable to 3D structure-based methods.
  arXiv  Detail & Related papers  (2022-04-01T16:39:16Z)
• LENS: Localization enhanced by NeRF synthesis [3.4386226615580107]
  We demonstrate improvement of camera pose regression thanks to an additional synthetic dataset rendered by the NeRF class of algorithm. We further improved localization accuracy of pose regressors using synthesized realistic and geometry consistent images as data augmentation during training.
  arXiv  Detail & Related papers  (2021-10-13T08:15:08Z)
• Deep Bingham Networks: Dealing with Uncertainty and Ambiguity in Pose Estimation [74.76155168705975]
  Deep Bingham Networks (DBN) can handle pose-related uncertainties and ambiguities arising in almost all real life applications concerning 3D data. DBN extends the state of the art direct pose regression networks by (i) a multi-hypotheses prediction head which can yield different distribution modes. We propose new training strategies so as to avoid mode or posterior collapse during training and to improve numerical stability.
  arXiv  Detail & Related papers  (2020-12-20T19:20:26Z)
• Leveraging Photometric Consistency over Time for Sparsely Supervised Hand-Object Reconstruction [118.21363599332493]
  We present a method to leverage photometric consistency across time when annotations are only available for a sparse subset of frames in a video. Our model is trained end-to-end on color images to jointly reconstruct hands and objects in 3D by inferring their poses. We achieve state-of-the-art results on 3D hand-object reconstruction benchmarks and demonstrate that our approach allows us to improve the pose estimation accuracy.
  arXiv  Detail & Related papers  (2020-04-28T12:03:14Z)

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.