  Indoor 3D object detection is an essential task in single image scene
understanding, impacting spatial cognition fundamentally in visual reasoning.
Existing works on 3D object detection from a single image either pursue this
goal through independent predictions of each object or implicitly reason over
all possible objects, failing to harness relational geometric information
between objects. To address this problem, we propose a dynamic sparse graph
pipeline named Explicit3D based on object geometry and semantics features.
Taking the efficiency into consideration, we further define a relatedness score
and design a novel dynamic pruning algorithm followed by a cluster sampling
method for sparse scene graph generation and updating. Furthermore, our
Explicit3D introduces homogeneous matrices and defines new relative loss and
corner loss to model the spatial difference between target pairs explicitly.
Instead of using ground-truth labels as direct supervision, our relative and
corner loss are derived from the homogeneous transformation, which renders the
model to learn the geometric consistency between objects. The experimental
results on the SUN RGB-D dataset demonstrate that our Explicit3D achieves
better performance balance than the-state-of-the-art.
