  Generalizable 3D object reconstruction from single-view RGB-D images remains
a challenging task, particularly with real-world data. Current state-of-the-art
methods develop Transformer-based implicit field learning, necessitating an
intensive learning paradigm that requires dense query-supervision uniformly
sampled throughout the entire space. We propose a novel approach, IPoD, which
harmonizes implicit field learning with point diffusion. This approach treats
the query points for implicit field learning as a noisy point cloud for
iterative denoising, allowing for their dynamic adaptation to the target object
shape. Such adaptive query points harness diffusion learning's capability for
coarse shape recovery and also enhances the implicit representation's ability
to delineate finer details. Besides, an additional self-conditioning mechanism
is designed to use implicit predictions as the guidance of diffusion learning,
leading to a cooperative system. Experiments conducted on the CO3D-v2 dataset
affirm the superiority of IPoD, achieving 7.8% improvement in F-score and 28.6%
in Chamfer distance over existing methods. The generalizability of IPoD is also
demonstrated on the MVImgNet dataset. Our project page is at
https://yushuang-wu.github.io/IPoD.
