Explicit3D: Graph Network with Spatial Inference for Single Image 3D Object Detection
This addresses indoor scene understanding for applications like robotics and AR/VR, but appears incremental as it builds on existing graph-based approaches with specific improvements.
The paper tackles the problem of single image 3D object detection by proposing Explicit3D, a graph network that explicitly models spatial relationships between objects, achieving a better performance balance than state-of-the-art methods on the SUN RGB-D dataset.
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.