Magnituder Layers for Implicit Neural Representations in 3D

• URL: http://arxiv.org/abs/2410.09771v1
• Date: Sun, 13 Oct 2024 08:06:41 GMT
• Title: Magnituder Layers for Implicit Neural Representations in 3D
• Authors: Sang Min Kim, Byeongchan Kim, Arijit Sehanobish, Krzysztof Choromanski, Dongseok Shim, Avinava Dubey, Min-hwan Oh,
• Abstract summary: We introduce a novel neural network layer called the "magnituder" By integrating magnituders into standard feed-forward layer stacks, we achieve improved inference speed and adaptability. Our approach enables a zero-shot performance boost in trained implicit neural representation models.
• Score: 23.135779936528333
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Improving the efficiency and performance of implicit neural representations in 3D, particularly Neural Radiance Fields (NeRF) and Signed Distance Fields (SDF) is crucial for enabling their use in real-time applications. These models, while capable of generating photo-realistic novel views and detailed 3D reconstructions, often suffer from high computational costs and slow inference times. To address this, we introduce a novel neural network layer called the "magnituder", designed to reduce the number of training parameters in these models without sacrificing their expressive power. By integrating magnituders into standard feed-forward layer stacks, we achieve improved inference speed and adaptability. Furthermore, our approach enables a zero-shot performance boost in trained implicit neural representation models through layer-wise knowledge transfer without backpropagation, leading to more efficient scene reconstruction in dynamic environments.

Related papers

• Neural NeRF Compression [19.853882143024]
  Recent NeRFs utilize feature grids to improve rendering quality and speed. These representations introduce significant storage overhead. This paper presents a novel method for efficiently compressing a grid-based NeRF model.
  arXiv  Detail & Related papers  (2024-06-13T09:12:26Z)
• Dynamic 3D Gaussian Fields for Urban Areas [60.64840836584623]
  We present an efficient neural 3D scene representation for novel-view synthesis (NVS) in large-scale, dynamic urban areas. We propose 4DGF, a neural scene representation that scales to large-scale dynamic urban areas.
  arXiv  Detail & Related papers  (2024-06-05T12:07:39Z)
• Scaffold-GS: Structured 3D Gaussians for View-Adaptive Rendering [71.44349029439944]
  Recent 3D Gaussian Splatting method has achieved the state-of-the-art rendering quality and speed. We introduce Scaffold-GS, which uses anchor points to distribute local 3D Gaussians. We show that our method effectively reduces redundant Gaussians while delivering high-quality rendering.
  arXiv  Detail & Related papers  (2023-11-30T17:58:57Z)
• VoxNeRF: Bridging Voxel Representation and Neural Radiance Fields for Enhanced Indoor View Synthesis [51.49008959209671]
  We introduce VoxNeRF, a novel approach that leverages volumetric representations to enhance the quality and efficiency of indoor view synthesis. We employ multi-resolution hash grids to adaptively capture spatial features, effectively managing occlusions and the intricate geometry of indoor scenes. We validate our approach against three public indoor datasets and demonstrate that VoxNeRF outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-11-09T11:32:49Z)
• VQ-NeRF: Vector Quantization Enhances Implicit Neural Representations [25.88881764546414]
  VQ-NeRF is an efficient pipeline for enhancing implicit neural representations via vector quantization. We present an innovative multi-scale NeRF sampling scheme that concurrently optimize the NeRF model at both compressed and original scales. We incorporate a semantic loss function to improve the geometric fidelity and semantic coherence of our 3D reconstructions.
  arXiv  Detail & Related papers  (2023-10-23T01:41:38Z)
• Deformable 3D Gaussians for High-Fidelity Monocular Dynamic Scene Reconstruction [29.83056271799794]
  Implicit neural representation has paved the way for new approaches to dynamic scene reconstruction and rendering. We propose a deformable 3D Gaussians Splatting method that reconstructs scenes using 3D Gaussians and learns them in canonical space. Through a differential Gaussianizer, the deformable 3D Gaussians not only achieve higher rendering quality but also real-time rendering speed.
  arXiv  Detail & Related papers  (2023-09-22T16:04:02Z)
• ResFields: Residual Neural Fields for Spatiotemporal Signals [61.44420761752655]
  ResFields is a novel class of networks specifically designed to effectively represent complex temporal signals. We conduct comprehensive analysis of the properties of ResFields and propose a matrix factorization technique to reduce the number of trainable parameters. We demonstrate the practical utility of ResFields by showcasing its effectiveness in capturing dynamic 3D scenes from sparse RGBD cameras.
  arXiv  Detail & Related papers  (2023-09-06T16:59:36Z)
• Explicifying Neural Implicit Fields for Efficient Dynamic Human Avatar Modeling via a Neural Explicit Surface [10.604108229704336]
  Implicit neural fields have advantages over traditional explicit representations in modeling dynamic 3D content. The paper proposes utilizing Neural Explicit Surface (NES) to explicitly represent implicit neural fields. NES performs similarly to previous 3D approaches, with greatly improved rendering speed and reduced memory cost.
  arXiv  Detail & Related papers  (2023-08-07T07:17:18Z)
• NeRFusion: Fusing Radiance Fields for Large-Scale Scene Reconstruction [50.54946139497575]
  We propose NeRFusion, a method that combines the advantages of NeRF and TSDF-based fusion techniques to achieve efficient large-scale reconstruction and photo-realistic rendering. We demonstrate that NeRFusion achieves state-of-the-art quality on both large-scale indoor and small-scale object scenes, with substantially faster reconstruction than NeRF and other recent methods.
  arXiv  Detail & Related papers  (2022-03-21T18:56:35Z)
• MVSNeRF: Fast Generalizable Radiance Field Reconstruction from Multi-View Stereo [52.329580781898116]
  We present MVSNeRF, a novel neural rendering approach that can efficiently reconstruct neural radiance fields for view synthesis. Unlike prior works on neural radiance fields that consider per-scene optimization on densely captured images, we propose a generic deep neural network that can reconstruct radiance fields from only three nearby input views via fast network inference.
  arXiv  Detail & Related papers  (2021-03-29T13:15:23Z)

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.