LOLNeRF: Learn from One Look

• URL: http://arxiv.org/abs/2111.09996v1
• Date: Fri, 19 Nov 2021 01:20:01 GMT
• Title: LOLNeRF: Learn from One Look
• Authors: Daniel Rebain, Mark Matthews, Kwang Moo Yi, Dmitry Lagun, Andrea Tagliasacchi
• Abstract summary: We present a method for learning a generative 3D model based on neural radiance fields. We show that, unlike existing methods, one does not need multi-view data to achieve this goal.
• Score: 22.771493686755544
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a method for learning a generative 3D model based on neural radiance fields, trained solely from data with only single views of each object. While generating realistic images is no longer a difficult task, producing the corresponding 3D structure such that they can be rendered from different views is non-trivial. We show that, unlike existing methods, one does not need multi-view data to achieve this goal. Specifically, we show that by reconstructing many images aligned to an approximate canonical pose with a single network conditioned on a shared latent space, you can learn a space of radiance fields that models shape and appearance for a class of objects. We demonstrate this by training models to reconstruct object categories using datasets that contain only one view of each subject without depth or geometry information. Our experiments show that we achieve state-of-the-art results in novel view synthesis and competitive results for monocular depth prediction.

Related papers

• A Fusion of Variational Distribution Priors and Saliency Map Replay for Continual 3D Reconstruction [1.2289361708127877]
  Single-image 3D reconstruction is a research challenge focused on predicting 3D object shapes from single-view images. This task requires significant data acquisition to predict both visible and occluded portions of the shape. We propose a continual learning-based 3D reconstruction method where our goal is to design a model using Variational Priors that can still reconstruct the previously seen classes reasonably even after training on new classes.
  arXiv  Detail & Related papers  (2023-08-17T06:48:55Z)
• 3D Surface Reconstruction in the Wild by Deforming Shape Priors from Synthetic Data [24.97027425606138]
  Reconstructing the underlying 3D surface of an object from a single image is a challenging problem. We present a new method for joint category-specific 3D reconstruction and object pose estimation from a single image. Our approach achieves state-of-the-art reconstruction performance across several real-world datasets.
  arXiv  Detail & Related papers  (2023-02-24T20:37:27Z)
• MegaPose: 6D Pose Estimation of Novel Objects via Render & Compare [84.80956484848505]
  MegaPose is a method to estimate the 6D pose of novel objects, that is, objects unseen during training. We present a 6D pose refiner based on a render&compare strategy which can be applied to novel objects. Second, we introduce a novel approach for coarse pose estimation which leverages a network trained to classify whether the pose error between a synthetic rendering and an observed image of the same object can be corrected by the refiner.
  arXiv  Detail & Related papers  (2022-12-13T19:30:03Z)
• Shape, Pose, and Appearance from a Single Image via Bootstrapped Radiance Field Inversion [54.151979979158085]
  We introduce a principled end-to-end reconstruction framework for natural images, where accurate ground-truth poses are not available. We leverage an unconditional 3D-aware generator, to which we apply a hybrid inversion scheme where a model produces a first guess of the solution. Our framework can de-render an image in as few as 10 steps, enabling its use in practical scenarios.
  arXiv  Detail & Related papers  (2022-11-21T17:42:42Z)
• Generative Deformable Radiance Fields for Disentangled Image Synthesis of Topology-Varying Objects [52.46838926521572]
  3D-aware generative models have demonstrated their superb performance to generate 3D neural radiance fields (NeRF) from a collection of monocular 2D images. We propose a generative model for synthesizing radiance fields of topology-varying objects with disentangled shape and appearance variations.
  arXiv  Detail & Related papers  (2022-09-09T08:44:06Z)
• AutoRF: Learning 3D Object Radiance Fields from Single View Observations [17.289819674602295]
  AutoRF is a new approach for learning neural 3D object representations where each object in the training set is observed by only a single view. We show that our method generalizes well to unseen objects, even across different datasets of challenging real-world street scenes.
  arXiv  Detail & Related papers  (2022-04-07T17:13:39Z)
• Unsupervised Learning of 3D Object Categories from Videos in the Wild [75.09720013151247]
  We focus on learning a model from multiple views of a large collection of object instances. We propose a new neural network design, called warp-conditioned ray embedding (WCR), which significantly improves reconstruction. Our evaluation demonstrates performance improvements over several deep monocular reconstruction baselines on existing benchmarks.
  arXiv  Detail & Related papers  (2021-03-30T17:57:01Z)
• ShaRF: Shape-conditioned Radiance Fields from a Single View [54.39347002226309]
  We present a method for estimating neural scenes representations of objects given only a single image. The core of our method is the estimation of a geometric scaffold for the object. We demonstrate in several experiments the effectiveness of our approach in both synthetic and real images.
  arXiv  Detail & Related papers  (2021-02-17T16:40:28Z)
• 3D Reconstruction of Novel Object Shapes from Single Images [23.016517962380323]
  We show that our proposed SDFNet achieves state-of-the-art performance on seen and unseen shapes. We provide the first large-scale evaluation of single image shape reconstruction to unseen objects.
  arXiv  Detail & Related papers  (2020-06-14T00:34:26Z)
• Self-supervised Single-view 3D Reconstruction via Semantic Consistency [142.71430568330172]
  We learn a self-supervised, single-view 3D reconstruction model that predicts the shape, texture and camera pose of a target object. The proposed method does not necessitate 3D supervision, manually annotated keypoints, multi-view images of an object or a prior 3D template.
  arXiv  Detail & Related papers  (2020-03-13T20:29:01Z)

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.