[2011.07233] Stable View Synthesis

We present Stable View Synthesis (SVS). Given a set of source images depicting a scene from freely distributed viewpoints, SVS synthesizes new views of the scene. The method operates on a geometric scaffold computed via structure-from-motion and multi-view stereo. Each point on this 3D scaffold is associated with view rays and corresponding feature vectors that encode the appearance of this point in the input images. The core of SVS is view-dependent on-surface feature aggregation, in which directional feature vectors at each 3D point are processed to produce a new feature vector for a ray that maps this point into the new target view. The target view is then rendered by a convolutional network from a tensor of features synthesized in this way for all pixels. The method is composed of differentiable modules and is trained end-to-end. It supports spatially-varying view-dependent importance weighting and feature transformation of source images at each point; spatial and temporal stabilit

2 mentions: @kcimc@artsiom_s
Date: 2021/02/21 02:21

Referring Tweets

@kcimc i missed this one: "stable view synthesis" from november 2020. no direct comparison to NSVF but looks as good if not better at first glance t.co/SkcB2kkBRm t.co/bYTgA1ogYZ
@artsiom_s Stable View Synthesis Given a set of source images depicting a scene from arbitrary viewpoints, it synthesizes new views of the scene. Structure-from-motion + multi-view stereo The results are magnificent! 🛠️t.co/2MOgyoS8BU 📝t.co/jQGQSFAXko t.co/xpR4mXrN67

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