Enhance-NeRF: Multiple Performance Evaluation for Neural Radiance Fields

• URL: http://arxiv.org/abs/2306.05303v1
• Date: Thu, 8 Jun 2023 15:49:30 GMT
• Title: Enhance-NeRF: Multiple Performance Evaluation for Neural Radiance Fields
• Authors: Qianqiu Tan, Tao Liu, Yinling Xie, Shuwan Yu, Baohua Zhang
• Abstract summary: Neural Radiance Fields (NeRF) can generate realistic images from any viewpoint. NeRF-based models are susceptible to interference issues caused by colored "fog" noise. Our approach, coined Enhance-NeRF, adopts joint color to balance low and high reflectivity objects display.
• Score: 2.5432277893532116
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The quality of three-dimensional reconstruction is a key factor affecting the effectiveness of its application in areas such as virtual reality (VR) and augmented reality (AR) technologies. Neural Radiance Fields (NeRF) can generate realistic images from any viewpoint. It simultaneously reconstructs the shape, lighting, and materials of objects, and without surface defects, which breaks down the barrier between virtuality and reality. The potential spatial correspondences displayed by NeRF between reconstructed scenes and real-world scenes offer a wide range of practical applications possibilities. Despite significant progress in 3D reconstruction since NeRF were introduced, there remains considerable room for exploration and experimentation. NeRF-based models are susceptible to interference issues caused by colored "fog" noise. Additionally, they frequently encounter instabilities and failures while attempting to reconstruct unbounded scenes. Moreover, the model takes a significant amount of time to converge, making it even more challenging to use in such scenarios. Our approach, coined Enhance-NeRF, which adopts joint color to balance low and high reflectivity objects display, utilizes a decoding architecture with prior knowledge to improve recognition, and employs multi-layer performance evaluation mechanisms to enhance learning capacity. It achieves reconstruction of outdoor scenes within one hour under single-card condition. Based on experimental results, Enhance-NeRF partially enhances fitness capability and provides some support to outdoor scene reconstruction. The Enhance-NeRF method can be used as a plug-and-play component, making it easy to integrate with other NeRF-based models. The code is available at: https://github.com/TANQIanQ/Enhance-NeRF

Related papers

• NeRF-Casting: Improved View-Dependent Appearance with Consistent Reflections [57.63028964831785]
  Recent works have improved NeRF's ability to render detailed specular appearance of distant environment illumination, but are unable to synthesize consistent reflections of closer content. We address these issues with an approach based on ray tracing. Instead of querying an expensive neural network for the outgoing view-dependent radiance at points along each camera ray, our model casts rays from these points and traces them through the NeRF representation to render feature vectors.
  arXiv  Detail & Related papers  (2024-05-23T17:59:57Z)
• Closing the Visual Sim-to-Real Gap with Object-Composable NeRFs [59.12526668734703]
  We introduce Composable Object Volume NeRF (COV-NeRF), an object-composable NeRF model that is the centerpiece of a real-to-sim pipeline. COV-NeRF extracts objects from real images and composes them into new scenes, generating photorealistic renderings and many types of 2D and 3D supervision.
  arXiv  Detail & Related papers  (2024-03-07T00:00:02Z)
• Reconstructive Latent-Space Neural Radiance Fields for Efficient 3D Scene Representations [34.836151514152746]
  In this work, we investigate combining an autoencoder with a NeRF, in which latent features are rendered and then convolutionally decoded. The resulting latent-space NeRF can produce novel views with higher quality than standard colour-space NeRFs. We can control the tradeoff between efficiency and image quality by shrinking the AE architecture, achieving over 13 times faster rendering with only a small drop in performance.
  arXiv  Detail & Related papers  (2023-10-27T03:52:08Z)
• From NeRFLiX to NeRFLiX++: A General NeRF-Agnostic Restorer Paradigm [57.73868344064043]
  We propose NeRFLiX, a general NeRF-agnostic restorer paradigm that learns a degradation-driven inter-viewpoint mixer. We also present NeRFLiX++ with a stronger two-stage NeRF degradation simulator and a faster inter-viewpoint mixer. NeRFLiX++ is capable of restoring photo-realistic ultra-high-resolution outputs from noisy low-resolution NeRF-rendered views.
  arXiv  Detail & Related papers  (2023-06-10T09:19:19Z)
• Multi-Space Neural Radiance Fields [74.46513422075438]
  Existing Neural Radiance Fields (NeRF) methods suffer from the existence of reflective objects. We propose a multi-space neural radiance field (MS-NeRF) that represents the scene using a group of feature fields in parallel sub-spaces. Our approach significantly outperforms the existing single-space NeRF methods for rendering high-quality scenes.
  arXiv  Detail & Related papers  (2023-05-07T13:11:07Z)
• Clean-NeRF: Reformulating NeRF to account for View-Dependent Observations [67.54358911994967]
  This paper proposes Clean-NeRF for accurate 3D reconstruction and novel view rendering in complex scenes. Clean-NeRF can be implemented as a plug-in that can immediately benefit existing NeRF-based methods without additional input.
  arXiv  Detail & Related papers  (2023-03-26T12:24:31Z)
• BAD-NeRF: Bundle Adjusted Deblur Neural Radiance Fields [9.744593647024253]
  We present a novel bundle adjusted deblur Neural Radiance Fields (BAD-NeRF) BAD-NeRF can be robust to severe motion blurred images and inaccurate camera poses. Our approach models the physical image formation process of a motion blurred image, and jointly learns the parameters of NeRF.
  arXiv  Detail & Related papers  (2022-11-23T10:53:37Z)
• 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)

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.