Omni-Scene: Omni-Gaussian Representation for Ego-Centric Sparse-View Scene Reconstruction

• URL: http://arxiv.org/abs/2412.06273v2
• Date: Thu, 27 Feb 2025 02:53:40 GMT
• Title: Omni-Scene: Omni-Gaussian Representation for Ego-Centric Sparse-View Scene Reconstruction
• Authors: Dongxu Wei, Zhiqi Li, Peidong Liu,
• Abstract summary: In autonomous driving scenarios, a more practical paradigm is ego-centric reconstruction, which is characterized by minimal cross-view overlap.<n>This paper conducts an in-depth analysis of different representations, and introduces Omni-Gaussian representation with tailored network design.<n> Experiments show that our method significantly surpasses state-of-the-art methods, pixelSplat and MVSplat, in ego-centric reconstruction.
• Score: 9.116550622312362
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Prior works employing pixel-based Gaussian representation have demonstrated efficacy in feed-forward sparse-view reconstruction. However, such representation necessitates cross-view overlap for accurate depth estimation, and is challenged by object occlusions and frustum truncations. As a result, these methods require scene-centric data acquisition to maintain cross-view overlap and complete scene visibility to circumvent occlusions and truncations, which limits their applicability to scene-centric reconstruction. In contrast, in autonomous driving scenarios, a more practical paradigm is ego-centric reconstruction, which is characterized by minimal cross-view overlap and frequent occlusions and truncations. The limitations of pixel-based representation thus hinder the utility of prior works in this task. In light of this, this paper conducts an in-depth analysis of different representations, and introduces Omni-Gaussian representation with tailored network design to complement their strengths and mitigate their drawbacks. Experiments show that our method significantly surpasses state-of-the-art methods, pixelSplat and MVSplat, in ego-centric reconstruction, and achieves comparable performance to prior works in scene-centric reconstruction.

Related papers

• Decompositional Neural Scene Reconstruction with Generative Diffusion Prior [64.71091831762214]
  Decompositional reconstruction of 3D scenes, with complete shapes and detailed texture, is intriguing for downstream applications. Recent approaches incorporate semantic or geometric regularization to address this issue, but they suffer significant degradation in underconstrained areas. We propose DP-Recon, which employs diffusion priors in the form of Score Distillation Sampling (SDS) to optimize the neural representation of each individual object under novel views.
  arXiv  Detail & Related papers  (2025-03-19T02:11:31Z)
• Few-Shot Multi-Human Neural Rendering Using Geometry Constraints [8.819403814092865]
  We present a method for recovering the shape and radiance of a scene consisting of multiple people given solely a few images. Existing approaches using implicit neural representations have achieved impressive results that deliver accurate geometry and appearance. We propose a neural implicit reconstruction method that addresses the inherent challenges of this task through the following contributions.
  arXiv  Detail & Related papers  (2025-02-11T00:10:58Z)
• Re-Visible Dual-Domain Self-Supervised Deep Unfolding Network for MRI Reconstruction [48.30341580103962]
  We propose a novel re-visible dual-domain self-supervised deep unfolding network to address these issues. We design a deep unfolding network based on Chambolle and Pock Proximal Point Algorithm (DUN-CP-PPA) to achieve end-to-end reconstruction. Experiments conducted on the fastMRI and IXI datasets demonstrate that our method significantly outperforms state-of-the-art approaches in terms of reconstruction performance.
  arXiv  Detail & Related papers  (2025-01-07T12:29:32Z)
• PVP-Recon: Progressive View Planning via Warping Consistency for Sparse-View Surface Reconstruction [49.7580491592023]
  We propose PVP-Recon, a novel and effective sparse-view surface reconstruction method. PVP-Recon starts initial surface reconstruction with as few as 3 views and progressively adds new views. This progressive view planning progress is interleaved with a neural SDF-based reconstruction module.
  arXiv  Detail & Related papers  (2024-09-09T10:06:34Z)
• Pixel-Aligned Multi-View Generation with Depth Guided Decoder [86.1813201212539]
  We propose a novel method for pixel-level image-to-multi-view generation. Unlike prior work, we incorporate attention layers across multi-view images in the VAE decoder of a latent video diffusion model. Our model enables better pixel alignment across multi-view images.
  arXiv  Detail & Related papers  (2024-08-26T04:56:41Z)
• VortSDF: 3D Modeling with Centroidal Voronoi Tesselation on Signed Distance Field [5.573454319150408]
  We introduce a volumetric optimization framework that combines explicit SDF fields with a shallow color network, in order to estimate 3D shape properties over tetrahedral grids. Experimental results with Chamfer statistics validate this approach with unprecedented reconstruction quality on various scenarios such as objects, open scenes or human.
  arXiv  Detail & Related papers  (2024-07-29T09:46:39Z)
• Towards Cross-View-Consistent Self-Supervised Surround Depth Estimation [9.569646683579899]
  Self-Supervised Surround Depth Estimation from consecutive images offers an economical alternative. Previous SSSDE methods have proposed different mechanisms to fuse information across images, but few of them explicitly consider the cross-view constraints. This paper proposes an efficient and consistent pose estimation design and two loss functions to enhance cross-view consistency for SSSDE.
  arXiv  Detail & Related papers  (2024-07-04T16:29:05Z)
• Object-level Scene Deocclusion [92.39886029550286]
  We present a new self-supervised PArallel visible-to-COmplete diffusion framework, named PACO, for object-level scene deocclusion. To train PACO, we create a large-scale dataset with 500k samples to enable self-supervised learning. Experiments on COCOA and various real-world scenes demonstrate the superior capability of PACO for scene deocclusion, surpassing the state of the arts by a large margin.
  arXiv  Detail & Related papers  (2024-06-11T20:34:10Z)
• 360 Layout Estimation via Orthogonal Planes Disentanglement and Multi-view Geometric Consistency Perception [56.84921040837699]
  Existing panoramic layout estimation solutions tend to recover room boundaries from a vertically compressed sequence, yielding imprecise results. We propose an orthogonal plane disentanglement network (termed DOPNet) to distinguish ambiguous semantics. We also present an unsupervised adaptation technique tailored for horizon-depth and ratio representations. Our solution outperforms other SoTA models on both monocular layout estimation and multi-view layout estimation tasks.
  arXiv  Detail & Related papers  (2023-12-26T12:16:03Z)
• Hyper-VolTran: Fast and Generalizable One-Shot Image to 3D Object Structure via HyperNetworks [53.67497327319569]
  We introduce a novel neural rendering technique to solve image-to-3D from a single view. Our approach employs the signed distance function as the surface representation and incorporates generalizable priors through geometry-encoding volumes and HyperNetworks. Our experiments show the advantages of our proposed approach with consistent results and rapid generation.
  arXiv  Detail & Related papers  (2023-12-24T08:42:37Z)
• HarmonicNeRF: Geometry-Informed Synthetic View Augmentation for 3D Scene Reconstruction in Driving Scenarios [2.949710700293865]
  HarmonicNeRF is a novel approach for outdoor self-supervised monocular scene reconstruction. It capitalizes on the strengths of NeRF and enhances surface reconstruction accuracy by augmenting the input space with geometry-informed synthetic views. Our approach establishes new benchmarks in synthesizing novel depth views and reconstructing scenes, significantly outperforming existing methods.
  arXiv  Detail & Related papers  (2023-10-09T07:42:33Z)
• Learning to Render Novel Views from Wide-Baseline Stereo Pairs [26.528667940013598]
  We introduce a method for novel view synthesis given only a single wide-baseline stereo image pair. Existing approaches to novel view synthesis from sparse observations fail due to recovering incorrect 3D geometry. We propose an efficient, image-space epipolar line sampling scheme to assemble image features for a target ray.
  arXiv  Detail & Related papers  (2023-04-17T17:40:52Z)
• Progressive Multi-view Human Mesh Recovery with Self-Supervision [68.60019434498703]
  Existing solutions typically suffer from poor generalization performance to new settings. We propose a novel simulation-based training pipeline for multi-view human mesh recovery.
  arXiv  Detail & Related papers  (2022-12-10T06:28:29Z)
• MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstruction [72.05649682685197]
  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.
  arXiv  Detail & Related papers  (2022-06-01T17:58:15Z)
• RISP: Rendering-Invariant State Predictor with Differentiable Simulation and Rendering for Cross-Domain Parameter Estimation [110.4255414234771]
  Existing solutions require massive training data or lack generalizability to unknown rendering configurations. We propose a novel approach that marries domain randomization and differentiable rendering gradients to address this problem. Our approach achieves significantly lower reconstruction errors and has better generalizability among unknown rendering configurations.
  arXiv  Detail & Related papers  (2022-05-11T17:59:51Z)
• Self-Supervised Video Object Segmentation via Cutout Prediction and Tagging [117.73967303377381]
  We propose a novel self-supervised Video Object (VOS) approach that strives to achieve better object-background discriminability. Our approach is based on a discriminative learning loss formulation that takes into account both object and background information. Our proposed approach, CT-VOS, achieves state-of-the-art results on two challenging benchmarks: DAVIS-2017 and Youtube-VOS.
  arXiv  Detail & Related papers  (2022-04-22T17:53:27Z)
• Weakly But Deeply Supervised Occlusion-Reasoned Parametric Layouts [87.370534321618]
  We propose an end-to-end network that takes a single perspective RGB image of a complex road scene as input, to produce occlusion-reasoned layouts in perspective space. The only human annotations required by our method are for parametric attributes that are cheaper and less ambiguous to obtain. We validate our approach on two public datasets, KITTI and NuScenes, to achieve state-of-the-art results with considerably lower human supervision.
  arXiv  Detail & Related papers  (2021-04-14T09:32:29Z)
• Spatially Consistent Representation Learning [12.120041613482558]
  We propose a spatially consistent representation learning algorithm (SCRL) for multi-object and location-specific tasks. We devise a novel self-supervised objective that tries to produce coherent spatial representations of a randomly cropped local region. On various downstream localization tasks with benchmark datasets, the proposed SCRL shows significant performance improvements.
  arXiv  Detail & Related papers  (2021-03-10T15:23:45Z)
• Monocular Real-Time Volumetric Performance Capture [28.481131687883256]
  We present the first approach to volumetric performance capture and novel-view rendering at real-time speed from monocular video. Our system reconstructs a fully textured 3D human from each frame by leveraging Pixel-Aligned Implicit Function (PIFu) We also introduce an Online Hard Example Mining (OHEM) technique that effectively suppresses failure modes due to the rare occurrence of challenging examples.
  arXiv  Detail & Related papers  (2020-07-28T04:45:13Z)

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.