  Monocular 3D reconstruction for categorical objects heavily relies on
accurately perceiving each object's pose. While gradient-based optimization in
a NeRF framework updates the initial pose, this paper highlights that
scale-depth ambiguity in monocular object reconstruction causes failures when
the initial pose deviates moderately from the true pose. Consequently, existing
methods often depend on a third-party 3D object to provide an initial object
pose, leading to increased complexity and generalization issues. To address
these challenges, we present SUP-NeRF, a Streamlined Unification of object Pose
estimation and NeRF-based object reconstruction. SUP-NeRF decouples the
object's dimension estimation and pose refinement to resolve the scale-depth
ambiguity, and introduces a camera-invariant projected-box representation that
generalizes cross different domains. While using a dedicated pose estimator
that smoothly integrates into an object-centric NeRF, SUP-NeRF is free from
external 3D detectors. SUP-NeRF achieves state-of-the-art results in both
reconstruction and pose estimation tasks on the nuScenes dataset. Furthermore,
SUP-NeRF exhibits exceptional cross-dataset generalization on the KITTI and
Waymo datasets, surpassing prior methods with up to 50\% reduction in rotation
and translation error.
