Topic
View synthesis
About: View synthesis is a research topic. Over the lifetime, 1701 publications have been published within this topic receiving 42333 citations.
Papers published on a yearly basis
Papers
More filters
••
01 Jan 2010TL;DR: This work proposes to only render a single image, together with a depth buffer and use image-based techniques to generate two individual images for the left and right eye, and computes a high-quality stereo pair for roughly half the cost of the traditional methods.
Abstract: Stereo vision is becoming increasingly popular in feature films, visualization and interactive applications such as computer games. However, computation costs are doubled when rendering an individual image for each eye. In this work, we propose to only render a single image, together with a depth buffer and use image-based techniques to generate two individual images for the left and right eye. The resulting method computes a high-quality stereo pair for roughly half the cost of the traditional methods. We achieve this result via an adaptive-grid warping that also involves information from previous frames to avoid artifacts.
59 citations
••
TL;DR: A novel approach based on Convolutional Neural Networks to jointly predict depth maps and foreground separation masks used to condition Generative Adversarial Networks for hallucinating plausible color and depths in the initially occluded areas is proposed.
59 citations
••
01 Jun 2021TL;DR: In this paper, the authors propose to represent multi-object dynamic scenes as scene graphs, which encodes object transformations and radiance, allowing them to efficiently render novel arrangements and views of the scene.
Abstract: Recent implicit neural rendering methods have demonstrated that it is possible to learn accurate view synthesis for complex scenes by predicting their volumetric density and color supervised solely by a set of RGB images. However, existing methods are restricted to learning efficient representations of static scenes that encode all scene objects into a single neural network, and they lack the ability to represent dynamic scenes and decompose scenes into individual objects. In this work, we present the first neural rendering method that represents multi-object dynamic scenes as scene graphs. We propose a learned scene graph representation, which encodes object transformations and radiance, allowing us to efficiently render novel arrangements and views of the scene. To this end, we learn implicitly encoded scenes, combined with a jointly learned latent representation to describe similar objects with a single implicit function. We assess the proposed method on synthetic and real automotive data, validating that our approach learns dynamic scenes – only by observing a video of this scene – and allows for rendering novel photo-realistic views of novel scene compositions with unseen sets of objects at unseen poses.
58 citations
••
23 Jun 2014TL;DR: This paper contributes a new physics-based generative model and the corresponding Maximum a Posteriori estimate, providing the desired unification between heuristics-based methods and a Bayesian formulation and shows that the novel Bayesian model significantly improves the quality of novel views, in particular if the scene geometry estimate is inaccurate.
Abstract: In this paper, we address the problem of synthesizing novel views from a set of input images. State of the art methods, such as the Unstructured Lumigraph, have been using heuristics to combine information from the original views, often using an explicit or implicit approximation of the scene geometry. While the proposed heuristics have been largely explored and proven to work effectively, a Bayesian formulation was recently introduced, formalizing some of the previously proposed heuristics, pointing out which physical phenomena could lie behind each. However, some important heuristics were still not taken into account and lack proper formalization. We contribute a new physics-based generative model and the corresponding Maximum a Posteriori estimate, providing the desired unification between heuristics-based methods and a Bayesian formulation. The key point is to systematically consider the error induced by the uncertainty in the geometric proxy. We provide an extensive discussion, analyzing how the obtained equations explain the heuristics developed in previous methods. Furthermore, we show that our novel Bayesian model significantly improves the quality of novel views, in particular if the scene geometry estimate is inaccurate.
58 citations
••
TL;DR: A new large-scale visual localization method targeted for indoor spaces that significantly outperforms current state-of-the-art indoor localization approaches on this new challenging data.
Abstract: We seek to predict the 6 degree-of-freedom (6DoF) pose of a query photograph with respect to a large indoor 3D map. The contributions of this work are three-fold. First, we develop a new large-scale visual localization method targeted for indoor spaces. The method proceeds along three steps: (i) efficient retrieval of candidate poses that scales to large-scale environments, (ii) pose estimation using dense matching rather than sparse local features to deal with weakly textured indoor scenes, and (iii) pose verification by virtual view synthesis that is robust to significant changes in viewpoint, scene layout, and occlusion. Second, we release a new dataset with reference 6DoF poses for large-scale indoor localization. Query photographs are captured by mobile phones at a different time than the reference 3D map, thus presenting a realistic indoor localization scenario. Third, we demonstrate that our method significantly outperforms current state-of-the-art indoor localization approaches on this new challenging data. Code and data are publicly available.
56 citations