In the field of vanishing point detection, previous works commonly relied on extracting and clustering straight lines or classifying candidate points as vanishing points. This paper proposes a novel end-to-end framework, called VPDETR (Vanishing Point DEtection TRansformer), that views vanishing point detection as a set prediction problem, applicable to both Manhattan and non-Manhattan world datasets. By using the positional embedding of anchor points as queries in Transformer decoders and dynamically updating them layer by layer, our method is able to directly input images and output their vanishing points without the need for explicit straight line extraction and candidate points sampling. Additionally, we introduce an orthogonal loss and a cross-prediction loss to improve accuracy on the Manhattan world datasets. Experimental results demonstrate that VPDETR achieves competitive performance compared to state-of-the-art methods, without requiring post-processing.

To find a suitable embedding for a knowledge graph (KG) remains a big challenge nowadays. By measuring the distance or plausibility of triples and quadruples in static and temporal knowledge graphs, many reliable knowledge graph embedding (KGE) models are proposed. However, these classical models may not be able to represent and infer various relation patterns well, such as TransE cannot represent symmetric relations, DistMult cannot represent inverse relations, RotatE cannot represent multiple relations, etc.. In this paper, we improve the ability of these models to represent various relation patterns by introducing the affine transformation framework. Specifically, we first utilize a set of affine transformations related to each relation or timestamp to operate on entity vectors, and then these transformed vectors can be applied not only to static KGE models, but also to temporal KGE models. The main advantage of using affine transformations is their good geometry properties with interpretability. Our experimental results demonstrate that the proposed intuitive design with affine transformations provides a statistically significant increase in performance with adding a few extra processing steps and keeping the same number of embedding parameters. Taking TransE as an example, we employ the scale transformation (the special case of an affine transformation). Surprisingly, it even outperforms RotatE to some extent on various datasets. We also introduce affine transformations into RotatE, Distmult, ComplEx, TTransE and TComplEx respectively, and experiments demonstrate that affine transformations consistently and significantly improve the performance of state-of-the-art KGE models on both static and temporal knowledge graph benchmarks.

In this work, we investigate contrastive learning on perturbed point clouds and find that the contrasting process may widen the domain gap caused by random perturbations, making the pre-trained network fail to generalize on testing data. To this end, we propose the Equivariant COntrastive framework which closes the domain gap before contrasting, further introduces the equivariance property, and enables pre-training networks under more perturbation types to obtain meaningful features. Specifically, to close the domain gap, a pre-trained VAE is adopted to convert perturbed point clouds into less perturbed point embedding of similar domains and separated perturbation embedding. The contrastive pairs can then be generated by mixing the point embedding with different perturbation embedding. Moreover, to pursue the equivariance property, a Vector Quantizer is adopted during VAE training, discretizing the perturbation embedding into one-hot tokens which indicate the perturbation labels. By correctly predicting the perturbation labels from the perturbed point cloud, the property of equivariance can be encouraged in the learned features. Experiments on synthesized and real-world perturbed datasets show that ECO-3D outperforms most existing pre-training strategies under various downstream tasks, achieving SOTA performance for lots of perturbations.

Domain adaptation for 3D point cloud has attracted a lot of interest since it can avoid the time-consuming labeling process of 3D data to some extent. A recent work named xMUDA leveraged multi-modal data to domain adaptation task of 3D semantic segmentation by mimicking the predictions between 2D and 3D modalities, and outperformed the previous single modality methods only using point clouds. Based on it, in this paper, we propose a novel cross-modal contrastive learning scheme to further improve the adaptation effects. By employing constraints from the correspondences between 2D pixel features and 3D point features, our method not only facilitates interaction between the two different modalities, but also boosts feature representations in both labeled source domain and unlabeled target domain. Meanwhile, to sufficiently utilize 2D context information for domain adaptation through cross-modal learning, we introduce a neighborhood feature aggregation module to enhance pixel features. The module employs neighborhood attention to aggregate nearby pixels in the 2D image, which relieves the mismatching between the two different modalities, arising from projecting relative sparse point cloud to dense image pixels. We evaluate our method on three unsupervised domain adaptation scenarios, including country-to-country, day-to-night, and dataset-to-dataset. Experimental results show that our approach outperforms existing methods, which demonstrates the effectiveness of the proposed method.

Deep learning based 3D point cloud classification and segmentation has achieved remarkable success. Existing methods are usually implemented in the original space with 3D coordinates as inputs. However, we find that point networks taking only information of first-order coordinates hardly learn geometric features of higher order, such as point cloud normals or poses. In this study, we propose to map the input point clouds into a non-linear space to facilitate networks learning and leveraging high-order features. Firstly, we design the Parametric Veronese Mapping (PVM) function which automatically learns to map point clouds into a non-linear space. As a result, the mapped point clouds are enriched with high-order elements and maintain the basic point set properties as in the original 3D space. We can then exploit existing networks to learn high-order features from mapped point clouds. Secondly, we contribute a two-stage transformation learning module that modifies the previous one-stage module to better leverage high-order features for aligning point clouds in the projective space. Finally, an interaction module is designed to learn more discriminative features by aggregating information from both the original and projective space. Extensive experiments demonstrate that our method successfully improves the ability of most existing networks to learn high-order features and thus contributing to more accurate classification and segmentation. Moreover, the resulting models show stronger robustness to affine transformations and real-world perturbations.

Knowledge graph embedding aims to represent entities and relations as low-dimensional vectors, which is an effective way for predicting missing links. It is crucial for knowledge graph embedding models to model and infer various relation patterns, such as symmetry/antisymmetry. However, many existing approaches fail to model semantic hierarchies, which are common in the real world. We propose a new model called HRQE, which represents entities as pure quaternions. The relational embedding consists of two parts: (a) Using unit quaternions to represent the rotation part in 3D space, where the head entities are rotated by the corresponding relations through Hamilton product. (b) Using scale parameters to constrain the modulus of entities to make them have hierarchical distributions. To the best of our knowledge, HRQE is the first model that can encode symmetry/antisymmetry, inversion, composition, multiple relation patterns and learn semantic hierarchies simultaneously. Experimental results demonstrate the effectiveness of HRQE against some of the SOTA methods on four well-established knowledge graph completion benchmarks.

Previous works on vanishing point detection usually use geometric prior for line segment clustering. We find that image context can also contribute to accurate line classification. Based on this observation, we propose to classify line segments into three groups according to three unknown-but-sought vanishing points with Manhattan world assumption, using both geometric information and image context in this work. To achieve this goal, we propose a novel Transformer based Line segment Classifier (TLC) that can group line segments in images and estimate the corresponding vanishing points. In TLC, we design a line segment descriptor to represent line segments using their positions, directions and local image contexts. Transformer based feature fusion module is used to capture global features from all line segments, which is proved to improve the classification performance significantly in our experiments. By using a network to score line segments for outlier rejection, vanishing points can be got by Singular Value Decomposition (SVD) from the classified lines. The proposed method runs at 25 fps on one NVIDIA 2080Ti card for vanishing point detection. Experimental results on synthetic and real-world datasets demonstrate that our method is superior to other state-of-the-art methods on the balance between accuracy and efficiency, while keeping stronger generalization capability when trained and evaluated on different datasets.