Depth-supervised NeRF: Fewer Views and Faster Training for Free

• URL: http://arxiv.org/abs/2107.02791v3
• Date: Thu, 17 Oct 2024 16:11:28 GMT
• Title: Depth-supervised NeRF: Fewer Views and Faster Training for Free
• Authors: Kangle Deng, Andrew Liu, Jun-Yan Zhu, Deva Ramanan,
• Abstract summary: DS-NeRF (Depth-supervised Neural Radiance Fields) is a loss for learning fields that takes advantage of readily-available depth supervision. We show that our loss is compatible with other recently proposed NeRF methods, demonstrating that depth is a cheap and easily digestible supervisory signal.
• Score: 69.34556647743285
• License:
• Abstract: A commonly observed failure mode of Neural Radiance Field (NeRF) is fitting incorrect geometries when given an insufficient number of input views. One potential reason is that standard volumetric rendering does not enforce the constraint that most of a scene's geometry consist of empty space and opaque surfaces. We formalize the above assumption through DS-NeRF (Depth-supervised Neural Radiance Fields), a loss for learning radiance fields that takes advantage of readily-available depth supervision. We leverage the fact that current NeRF pipelines require images with known camera poses that are typically estimated by running structure-from-motion (SFM). Crucially, SFM also produces sparse 3D points that can be used as "free" depth supervision during training: we add a loss to encourage the distribution of a ray's terminating depth matches a given 3D keypoint, incorporating depth uncertainty. DS-NeRF can render better images given fewer training views while training 2-3x faster. Further, we show that our loss is compatible with other recently proposed NeRF methods, demonstrating that depth is a cheap and easily digestible supervisory signal. And finally, we find that DS-NeRF can support other types of depth supervision such as scanned depth sensors and RGB-D reconstruction outputs.

Related papers

• Depth-guided NeRF Training via Earth Mover's Distance [0.6749750044497732]
  We propose a novel approach to uncertainty in depth priors for NeRF supervision. We use off-the-shelf pretrained diffusion models to predict depth and capture uncertainty during the denoising process. Our depth-guided NeRF outperforms all baselines on standard depth metrics by a large margin.
  arXiv  Detail & Related papers  (2024-03-19T23:54:07Z)
• NeRF-Det++: Incorporating Semantic Cues and Perspective-aware Depth Supervision for Indoor Multi-View 3D Detection [72.0098999512727]
  NeRF-Det has achieved impressive performance in indoor multi-view 3D detection by utilizing NeRF to enhance representation learning. We present three corresponding solutions, including semantic enhancement, perspective-aware sampling, and ordinal depth supervision. The resulting algorithm, NeRF-Det++, has exhibited appealing performance in the ScanNetV2 and AR KITScenes datasets.
  arXiv  Detail & Related papers  (2024-02-22T11:48:06Z)
• SimpleNeRF: Regularizing Sparse Input Neural Radiance Fields with Simpler Solutions [6.9980855647933655]
  supervising the depth estimated by the NeRF helps train it effectively with fewer views. We design augmented models that encourage simpler solutions by exploring the role of positional encoding and view-dependent radiance. We achieve state-of-the-art view-synthesis performance on two popular datasets by employing the above regularizations.
  arXiv  Detail & Related papers  (2023-09-07T18:02:57Z)
• ViP-NeRF: Visibility Prior for Sparse Input Neural Radiance Fields [9.67057831710618]
  Training neural radiance fields (NeRFs) on sparse input views leads to overfitting and incorrect scene depth estimation. We reformulate the NeRF to also directly output the visibility of a 3D point from a given viewpoint to reduce the training time with the visibility constraint. Our model outperforms the competing sparse input NeRF models including those that use learned priors.
  arXiv  Detail & Related papers  (2023-04-28T18:26:23Z)
• SparseNeRF: Distilling Depth Ranking for Few-shot Novel View Synthesis [93.46963803030935]
  We present a new Sparse-view NeRF (SparseNeRF) framework that exploits depth priors from real-world inaccurate observations. We propose a simple yet effective constraint, a local depth ranking method, on NeRFs such that the expected depth ranking of the NeRF is consistent with that of the coarse depth maps in local patches. We also collect a new dataset NVS-RGBD that contains real-world depth maps from Azure Kinect, ZED 2, and iPhone 13 Pro.
  arXiv  Detail & Related papers  (2023-03-28T17:58:05Z)
• 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)
• AligNeRF: High-Fidelity Neural Radiance Fields via Alignment-Aware Training [100.33713282611448]
  We conduct the first pilot study on training NeRF with high-resolution data. We propose the corresponding solutions, including marrying the multilayer perceptron with convolutional layers. Our approach is nearly free without introducing obvious training/testing costs.
  arXiv  Detail & Related papers  (2022-11-17T17:22:28Z)
• Aug-NeRF: Training Stronger Neural Radiance Fields with Triple-Level Physically-Grounded Augmentations [111.08941206369508]
  We propose Augmented NeRF (Aug-NeRF), which for the first time brings the power of robust data augmentations into regularizing the NeRF training. Our proposal learns to seamlessly blend worst-case perturbations into three distinct levels of the NeRF pipeline. Aug-NeRF effectively boosts NeRF performance in both novel view synthesis and underlying geometry reconstruction.
  arXiv  Detail & Related papers  (2022-07-04T02:27:07Z)

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.