Point cloud-based 3-D object detection is a significant and critical issue in numerous applications. While most existing methods attempt to capitalize on the geometric characteristics of point clouds, they neglect the internal semantic properties of point and the consistency between the semantic and geometric clues. We introduce a semantic consistency (SC) mechanism for 3-D object detection in this article, by reasoning about the semantic relations between 3-D object boxes and its internal points. This mechanism is based on a natural principle: the semantic category of a 3-D bounding box should be consistent with the categories of all points within the box. Driven by the SC mechanism, we propose a novel SC network (SCNet) to detect 3-D objects from point clouds. Specifically, the SCNet is composed of a feature extraction module, a detection decision module, and a semantic segmentation module. In inference, the feature extraction and the detection decision modules are used to detect 3-D objects. In training, the semantic segmentation module is jointly trained with the other two modules to produce more robust and applicable model parameters. The performance is greatly boosted through reasoning about the relations between the output 3-D object boxes and segmented points. The proposed SC mechanism is model-agnostic and can be integrated into other base 3-D object detection models. We test the proposed model on three challenging indoor and outdoor benchmark datasets: ScanNetV2, SUN RGB-D, and KITTI. Furthermore, to validate the universality of the SC mechanism, we implement it in three different 3-D object detectors. The experiments show that the performance is impressively improved and the extensive ablation studies also demonstrate the effectiveness of the proposed model.

Scene graph generation is a significant and challenging task for scene understanding. Most existing methods are confined to the 2D space (i.e. images) or additional use of segmentation information, while neglecting the richer spatial and geometric information of 3D space. In this paper, we propose a novel method to generate scene graphs from 3D point clouds. Specifically, our model consists of three parts: a point feature extraction backbone, a box head, and a relation head. The feature extraction backbone extracts base features directly from raw point clouds, and the box head produces detected 3D bounding boxes. Final 3D scene graphs are obtained from the relation head which takes the extracted features and 3D boxes as inputs. We also design a point RoI module which sequentially processes points inside 3D boxes with a bidirectional LSTM. To further leverage the geometric characteristics of point clouds, we propose a location attention module which learns the influence of relative locations between objects. We introduce the RelationScanNet dataset with densely annotated semantic and geometric relationships, which extends one of the most widely used dataset ScanNetV2 in 3D indoor scene understanding. We test the proposed method on the RelationScanNet dataset and 3DSSG dataset. The results prove the strength of our method.

Detecting 3D objects from point clouds is a significant yet challenging issue in many applications. While most existing approaches seek to leverage geometric information of point clouds, few studies accommodate the inherent semantic characteristics of each point and the consistency between the geometric and semantic cues. In this work, we propose a novel semantic consistency network (SCNet) driven by a natural principle: the class of a predicted 3D bounding box should be consistent with the classes of all the points inside this box. Specifically, our SCNet consists of a feature extraction structure, a detection decision structure, and a semantic segmentation structure. In inference, the feature extraction and the detection decision structures are used to detect 3D objects. In training, the semantic segmentation structure is jointly trained with the other two structures to produce more robust and applicative model parameters. A novel semantic consistency loss is proposed to regulate the output 3D object boxes and the segmented points to boost the performance. Our model is evaluated on two challenging datasets and achieves comparable results to the state-of-the-art methods.