  Neural Radiance Field (NeRF) has emerged as a compelling method to represent
3D objects and scenes for photo-realistic rendering. However, its implicit
representation causes difficulty in manipulating the models like the explicit
mesh representation. Several recent advances in NeRF manipulation are usually
restricted by a shared renderer network, or suffer from large model size. To
circumvent the hurdle, in this paper, we present an explicit neural field
representation that enables efficient and convenient manipulation of models. To
achieve this goal, we learn a hybrid tensor rank decomposition of the scene
without neural networks. Motivated by the low-rank approximation property of
the SVD algorithm, we propose a rank-residual learning strategy to encourage
the preservation of primary information in lower ranks. The model size can then
be dynamically adjusted by rank truncation to control the levels of detail,
achieving near-optimal compression without extra optimization. Furthermore,
different models can be arbitrarily transformed and composed into one scene by
concatenating along the rank dimension. The growth of storage cost can also be
mitigated by compressing the unimportant objects in the composed scene. We
demonstrate that our method is able to achieve comparable rendering quality to
state-of-the-art methods, while enabling extra capability of compression and
composition. Code will be made available at https://github.com/ashawkey/CCNeRF.
