This study explores single-image zero-shot 3D shape reconstruction, contrasting computationally intensive generative modeling approaches with more efficient regression-based methods to evaluate performance.
The ZeroShape model is developed based on the integration of successful regression techniques and novel insights, achieving superior performance in 3D shape reconstruction.
We introduce a diverse and extensive evaluation benchmark consisting of various realistic 3D object datasets that significantly outperforms previous benchmarks in terms of scale and evaluation variance.
Findings indicate that regression-based methods can still compete effectively, challenging the notion that state-of-the-art generative models are necessary for optimal results in 3D shape reconstruction.
#computer-vision #3d-shape-reconstruction #zero-shot-learning #deep-learning-models #evaluation-benchmark
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