Reconstructing category-specific objects from a single image is a challenging
task that requires inferring the geometry and appearance of an object from a
limited viewpoint. Existing methods typically rely on local feature retrieval
based on re-projection with known camera intrinsic, which are slow and prone to
distortion at viewpoints distant from the input image. In this paper, we
present Variable Radiance Field (VRF), a novel framework that can efficiently
reconstruct category-specific objects from a single image without known camera
parameters. Our key contributions are: (1) We parameterize the geometry and
appearance of the object using a multi-scale global feature extractor, which
avoids frequent point-wise feature retrieval and camera dependency. We also
propose a contrastive learning-based pretraining strategy to improve the
feature extractor. (2) We reduce the geometric complexity of the object by
learning a category template, and use hypernetworks to generate a small neural
radiance field for fast and instance-specific rendering. (3) We align each
training instance to the template space using a learned similarity
transformation, which enables semantic-consistent learning across different
objects. We evaluate our method on the CO3D dataset and show that it
outperforms existing methods in terms of quality and speed. We also demonstrate
its applicability to shape interpolation and object placement tasks.

利用 Variable Radiance Field 框架，通过多尺度全局特征提取器参数化物体的几何和外观，学习类别模板并使用超网络生成小型神经辐射场，实现从单张图像高效、准确地重建特定类别物体。