Problem
Research questions and friction points this paper is trying to address.
Stereo Reconstruction
Inductive Bias
Vision Transformer
Synthetic Datasets
Data-Driven Learning
NBS: No Bias Stereo
🤖 AI Summary
本文挑战了立体重建需要强架构归纳偏置的观点，提出使用无偏置的视觉变换器通过大规模合成数据训练来实现高精度和高效运行。
📝 Abstract
Stereo reconstruction is one of the last remaining Computer Vision tasks where all state-of-the-art methods employ a heavy architectural inductive bias. Even though it has been demonstrated that the task can be solved using general-purpose methods, it is widely believed that inductive biases in stereo are strictly necessary for both high-quality results and computational efficiency. We challenge this paradigm. In this paper, we demonstrate that both state-of-the-art accuracy and superior runtime efficiency are achievable with a model completely devoid of architectural inductive biases, relying instead on a simple, end-to-end Vision Transformer. By training on massive synthetic datasets, we show that pure data-driven learning can surpass explicitly engineered geometry. This work proves that explicit inductive biases are no longer a prerequisite for stereo matching, ultimately unlocking true scaling laws for continuous improvement in 3D reconstruction.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Stereo Reconstruction
Vision Transformer
Data-Driven Learning
Inductive Bias
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