Explicit3D: Graph Network with Spatial Inference for Single Image 3D Object Detection

TL;DR

Explicit3D introduces a graph-based approach with spatial inference for single image 3D object detection, leveraging relational geometry and semantics to improve accuracy and efficiency in indoor scene understanding.

Contribution

The paper presents a novel dynamic sparse graph pipeline with a pruning algorithm and new loss functions that explicitly model spatial relationships between objects.

Findings

Outperforms state-of-the-art on SUN RGB-D dataset

Balances detection accuracy and computational efficiency

Effectively models geometric consistency between objects

Abstract

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.