Stable View Synthesis (2011.07233v2)

Abstract: 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 stability due to the smooth dependence of on-surface feature aggregation on the target view; and synthesis of view-dependent effects such as specular reflection. Experimental results demonstrate that SVS outperforms state-of-the-art view synthesis methods both quantitatively and qualitatively on three diverse real-world datasets, achieving unprecedented levels of realism in free-viewpoint video of challenging large-scale scenes. Code is available at https://github.com/intel-isl/StableViewSynthesis

• The paper presents a method that constructs a 3D geometric scaffold with SfM and MVS to produce novel, photorealistic views.
• The approach employs view-dependent feature aggregation using permutation-invariant operations for consistent deep feature synthesis.
• The method achieves notable improvements, reducing LPIPS error by around 30% on Tanks and Temples and 7% on the FVS dataset.

Insights into "Stable View Synthesis"

The paper "Stable View Synthesis" by Gernot Riegler and Vladlen Koltun presents Stable View Synthesis (SVS), a method aimed at producing photorealistic views from arbitrary viewpoints based on a set of unlabeled input images. This approach demonstrates significant advancements in spatial and temporal coherence in the synthesis of realistic scenes.

At the heart of SVS is its novel approach to feature aggregation. The method constructs a geometric scaffold of the scene using conventional structure-from-motion (SfM) and multi-view stereo (MVS) techniques. Each point on this 3D scaffold is associated with view rays and deep feature vectors from input images that visually capture the point. A noteworthy contribution of the method is the aggregating of these directional feature vectors to form a consistent view-dependent representation, enabling the realistic synthesis of new target views through a convolutional network applied to the resulting feature tensor.

Core Methodology

SVS introduces a differentiable, end-to-end trainable pipeline composed of several key components:

• Geometric Scaffold: A 3D representation of the scene, constructed using SfM and MVS.
• Feature Encoding and Aggregation: Input images are processed to create deep feature representations. The crucial step is the view-dependent feature aggregation, which harnesses permutation-invariant operations to synthesize new feature vectors for rendering.
• Rendering: The new image is rendered from the aggregated feature tensor through a learned convolutional network, maintaining spatial and temporal stability across synthesized viewpoints.

The method efficiently addresses common challenges in view synthesis, such as handling specular reflections and view-dependent effects, by processing information from all relevant input images simultaneously without heuristic selection. This ensures consistent and coherent synthesized outputs.

Experimental Evaluation

SVS is tested across various datasets, notably Tanks and Temples, the FVS dataset, and DTU. Experimental results show SVS outperforming the current state-of-the-art both qualitatively and quantitatively. On the Tanks and Temples dataset, SVS reduces the LPIPS error by approximately 30% on average compared to prior methods. Similarly, on the FVS dataset, a 7% reduction in LPIPS is achieved, underscoring the method's superior photorealism and stability.

Implications and Future Work

This work evidences substantial progress in the domain of view synthesis, with SVS setting new benchmarks for photorealistic rendering of complex scenes. The method's robustness and efficiency potentially broaden its applicability in various practical scenarios, such as AR/VR environments, remote exploration, and film production.

Looking forward, the authors suggest several avenues for future exploration. Advancements in the accuracy of 3D reconstruction technologies could further enhance SVS's fidelity, while extending capabilities to syntheses under varying lighting conditions may enable more dynamic scene rendering. Moreover, overcoming limitations inherent in static scene handling could pave the way for interactive and manipulable environments, thus enriching user experience and interaction.

In conclusion, "Stable View Synthesis" presents a noteworthy step in improving view synthesis by bridging disparate inputs into a coherent, photorealistic output. It opens intriguing possibilities for future research, particularly in areas merging computer vision with interactive graphics.