Quark: Real-time, High-resolution, and General Neural View Synthesis (2411.16680v1)

Abstract: We present a novel neural algorithm for performing high-quality, high-resolution, real-time novel view synthesis. From a sparse set of input RGB images or videos streams, our network both reconstructs the 3D scene and renders novel views at 1080p resolution at 30fps on an NVIDIA A100. Our feed-forward network generalizes across a wide variety of datasets and scenes and produces state-of-the-art quality for a real-time method. Our quality approaches, and in some cases surpasses, the quality of some of the top offline methods. In order to achieve these results we use a novel combination of several key concepts, and tie them together into a cohesive and effective algorithm. We build on previous works that represent the scene using semi-transparent layers and use an iterative learned render-and-refine approach to improve those layers. Instead of flat layers, our method reconstructs layered depth maps (LDMs) that efficiently represent scenes with complex depth and occlusions. The iterative update steps are embedded in a multi-scale, UNet-style architecture to perform as much compute as possible at reduced resolution. Within each update step, to better aggregate the information from multiple input views, we use a specialized Transformer-based network component. This allows the majority of the per-input image processing to be performed in the input image space, as opposed to layer space, further increasing efficiency. Finally, due to the real-time nature of our reconstruction and rendering, we dynamically create and discard the internal 3D geometry for each frame, generating the LDM for each view. Taken together, this produces a novel and effective algorithm for view synthesis. Through extensive evaluation, we demonstrate that we achieve state-of-the-art quality at real-time rates. Project page: https://quark-3d.github.io/

• The paper introduces a novel neural algorithm that achieves real-time high-resolution (1080p, 30 fps) view synthesis across diverse scenes.
• It employs layered depth maps with a multi-scale UNet-style architecture and Transformer-based fusion to efficiently integrate multi-view data.
• Extensive evaluations show state-of-the-art performance on static and dynamic scenes, paving the way for applications in VR, AR, and live broadcasting.

Quark: Real-Time, High-Resolution, and General Neural View Synthesis

The paper "Quark: Real-time, High-resolution, and General Neural View Synthesis" introduces a novel neural algorithm that proficiently tackles the challenge of rendering high-quality, high-resolution novel views in real-time. From a sparse selection of input images or video streams, Quark not only reconstructs 3D scenes but also renders them at 1080p resolution and 30 frames per second, using an NVIDIA A100 GPU. The network displays generalization potential across varied datasets and scenes, achieving state-of-the-art results for a real-time method and at times surpassing the quality of some existing offline methods.

The innovative approach of Quark is underpinned by several key concepts combined into a coherent algorithmic framework. Firstly, instead of employing flat scene layers, Quark constructs Layered Depth Maps (LDMs) that effectively represent complex depths and occlusions. This approach enhances representation efficiency, particularly for scenes with intricate geometry and overlapping surfaces.

Secondly, Quark employs a multi-scale, UNet-style architecture, providing a foundation for conducting computational steps at reduced resolution. This aspect of the algorithm significantly increases processing efficiency. The iterative update steps are embedded in this framework to optimize computational overhead while maintaining high output quality.

A notable feature of Quark is its Transformer-based component within each update step, which is designed to aggregate information from multiple input views more effectively. This innovation allows much of the per-input image processing to occur in the image space rather than the layer space, thus boosting overall efficiency. Such sophisticated use of Transformers for view fusion signifies a major advancement in neural rendering capabilities by enhancing the network's capacity to integrate multi-view data robustly.

A critical aspect of Quark's design is its real-time processing capability. The network dynamically constructs and discards internal 3D geometry for each frame, creating the LDM specific to each viewpoint. This dynamic approach not only supports efficient computation but also allows for responsive adaptability to continuously changing input scenarios, such as dynamic content within scenes.

Through extensive evaluation, Quark demonstrates its capacity to achieve real-time rendering with superior visual quality. The paper provides a thorough exploration of the algorithm's performance, showing state-of-the-art results on several standard benchmarks for novel view synthesis and specific datasets involving both static and dynamic scenes.

The implications of this research in the fields of computer graphics and neural rendering are substantial, offering a viable path towards real-time applications in virtual reality, augmented reality, and live event broadcasting, where high-resolution rendering at low latency is crucial. Theoretically, Quark contributes to the discourse on efficient neural rendering architectures, particularly highlighting the potential of Transformer networks for effective multi-view synthesis.

While the results are compelling, there is a scope for future work, particularly in enhancing the strategies for view-dependent effects and further optimizing the network for lower power, edge-compute devices. Continued exploration into reducing temporal flickering and improving robustness to camera calibration errors may also provide fruitful avenues for advancing the research introduced by Quark.

Overall, the paper "Quark: Real-time, High-resolution, and General Neural View Synthesis" presents an insightful and technically sophisticated contribution to the state-of-the-art in high-quality neural view synthesis.