Efficient-LVSM: Faster, Cheaper, and Better Large View Synthesis Model via Decoupled Co-Refinement Attention
#Efficient-LVSM #Novel View Synthesis #Transformer Models #Decoupled Attention #Deep Learning #arXiv #3D Reconstruction
📌 Key Takeaways
- Efficient-LVSM introduces a dual-stream architecture to replace standard full self-attention in 3D view synthesis.
- The model solves the problem of quadratic complexity, allowing it to handle more input views with less computational power.
- Decoupled Co-Refinement Attention allows for more flexible processing of heterogeneous data tokens.
- The new framework provides a more cost-effective and faster solution for generating high-quality novel views from limited sets of images.
📖 Full Retelling
Researchers specializing in computer vision and artificial intelligence published a technical paper on the arXiv preprint server on February 10, 2025, detailing a new architecture called Efficient-LVSM designed to optimize Large View Synthesis Models (LVSM). This development addresses the inherent performance bottlenecks and high computational costs found in existing transformer-based feedforward models, which are used to generate novel cinematic views from limited input data. By introducing a dual-stream architecture, the team aims to overcome the technical limitations of traditional self-attention mechanisms that have previously hindered the scalability of high-quality 3D scene reconstruction.
The core innovation of Efficient-LVSM lies in its "Decoupled Co-Refinement Attention" mechanism. Traditional LVSM frameworks utilize a full self-attention design, which requires calculating relationships between every input and target view simultaneously. While effective for image quality, this approach suffers from quadratic complexity, meaning that as the number of input views increases, the required processing power and memory usage grow exponentially. The researchers argue that this monolithic design is suboptimal because it forces rigid parameter sharing among heterogeneous tokens, which are fundamentally different pieces of visual data that should be processed with more flexibility.
Efficient-LVSM operates by decoupling these processes into a dual-stream system, allowing for a more streamlined flow of information that reduces the heavy computational load. This architectural shift enables the model to be "faster, cheaper, and better," effectively lowering the hardware barrier for sophisticated novel view synthesis. By optimizing how the model attends to different visual inputs, the researchers have managed to maintain high fidelity in the synthesized images while significantly improving the inference speed and reducing the carbon footprint associated with training large-scale transformer models.
🏷️ Themes
Artificial Intelligence, Computer Vision, Machine Learning
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📄 Original Source Content
arXiv:2602.06478v1 Announce Type: cross Abstract: Feedforward models for novel view synthesis (NVS) have recently advanced by transformer-based methods like LVSM, using attention among all input and target views. In this work, we argue that its full self-attention design is suboptimal, suffering from quadratic complexity with respect to the number of input views and rigid parameter sharing among heterogeneous tokens. We propose Efficient-LVSM, a dual-stream architecture that avoids these issues