In this paper, we address the 3D object detection task by capturing multi-level contextual information with the selfattention mechanism and multi-scale feature fusion. Most existing 3D object detection methods recognize objects individually, without giving any consideration on contextual information between these objects. Comparatively, we propose Multi-Level Context VoteNet (MLCVNet) to recognize 3D objects correlatively, building on the state-of-the-art VoteNet. We introduce three context modules into the voting and classifying stages of VoteNet to encode contextual information at different levels. Specifically, a Patch-to-Patch Context (PPC) module is employed to capture contextual information between the point patches, before voting for their corresponding object centroid points. Subsequently, an Object-to-Object Context (OOC) module is incorporated before the proposal and classification stage, to capture the contextual information between object candidates. Finally, a Global Scene Context (GSC) module is designed to learn the global scene context. We demonstrate these by capturing contextual information at patch, object and scene levels. Our method is an effective way to promote detection accuracy, achieving new state-of-the-art detection performance on challenging 3D object detection datasets, i.e., SUN RGBD and ScanNet. We also release our code at https://github.com/NUAAXQ/MLCVNet.

Benefiting from the excellent performance of Siamesebased trackers, huge progress on 2D visual tracking has been achieved. However, 3D visual tracking is still underexplored. Inspired by the idea of Hough voting in 3D object detection, in this paper, we propose a Multi-level Voting Siamese Network (MLVSNet) for 3D visual tracking from outdoor point cloud sequences. To deal with sparsity in outdoor 3D point clouds, we propose to perform Hough voting on multi-level features to get more vote centers and retain more useful information, instead of voting only on the final level feature as in previous methods. We also design an efficient and lightweight Target-Guided Attention (TGA) module to transfer the target information and highlight the target points in the search area. Moreover, we propose a Vote-cluster Feature Enhancement (VFE) module to exploit the relationships between different vote clusters. Extensive experiments on the 3D tracking benchmark of KITTI dataset demonstrate that our MLVSNet outperforms state-of-the-art methods with significant margins. Code will be available at https://github.com/CodeWZT/MLVSNet.

Hough voting, as has been demonstrated in VoteNet, is effective for 3D object detection, where voting is a key step. In this paper, we propose a novel VoteNet-based 3D detector with vote enhancement to improve the detection accuracy in cluttered indoor scenes. It addresses the limitations of current voting schemes, i.e., votes from neighboring objects and background have significant negative impacts. Before voting, we replace the classic MLP with the proposed Attentive MLP (AMLP) in the backbone network to get better feature description of seed points. During voting, we design a new vote attraction loss (VALoss) to enforce vote centers to locate closely and compactly to the corresponding object centers. After voting, we then devise a vote weighting module to integrate the foreground/background prediction into the vote aggregation process to enhance the capability of the original VoteNet to handle noise from background voting. The three proposed strategies all contribute to more effective voting and improved performance, resulting in a novel 3D object detector, termed VENet. Experiments show that our method outperforms state-of-the-art methods on benchmark datasets. Ablation studies demonstrate the effectiveness of the proposed components.

In this article, we propose a Multi-feature Fusion VoteNet (MFFVoteNet) framework for improving the 3D object detection performance in cluttered and heavily occluded scenes. Our method takes the point cloud and the synchronized RGB image as inputs to provide object detection results in 3D space. Our detection architecture is built on VoteNet with three key designs. First, we augment the VoteNet input with point color information to enhance the difference of various instances in a scene. Next, we integrate an image feature module into the VoteNet to provide a strong object class signal that can facilitate deterministic detections in occlusion. Moreover, we propose a Projection Non-Maximum Suppression (PNMS) method in 3D object detection to eliminate redundant proposals and hence provide more accurate positioning of 3D objects. We evaluate the proposed MFFVoteNet on two challenging 3D object detection datasets, i.e., ScanNetv2 and SUN RGB-D. Extensive experiments show that our framework can effectively improve the performance of 3D object detection.

Most existing 3D object detection methods recognize objects individually, without giving any consideration on contextual information between these objects. However, objects in indoor scenes are usually related to each other and the scene, forming the contextual information. Based on this observation, we propose a novel 3D object detection network, which is built on the state-of-the-art VoteNet but takes into consideration of the contextual information at multiple levels for detection and recognition of 3D objects. To encode relationships between elements at different levels, we introduce three contextual sub-modules, capturing contextual information at patch, object, and scene levels respectively, and build them into the voting and classification stages of VoteNet. In addition, at the post-processing stage, we also consider the spatial diversity of detected objects and propose an improved 3D NMS (non-maximum suppression) method, namely Survival-Of-the-Best 3DNMS (SOB-3DNMS), to reduce false detections. Experiments demonstrate that our method is an effective way to promote detection accuracy, and has achieved new state-of-the-art detection performance on challenging 3D object detection datasets, i.e., SUN RGBD and ScanNet, when only taking point cloud data as input.