Existing 3D single object trackers (SOTs) of a point cloud all apply downscaling when extracting features from points. This operation leads to a loss of spatial and structural information, degrading tracking performance of sparsely distributed and small-scale objects. To address this problem, a structure aware SOT of a point cloud is proposed. Specifically, the backbone network is combined with the auxiliary network to learn point-wise representations. During the training stage, the subsidiary network is used to perform additional tasks and supervisions, which guides the backbone network to extract discriminative structural features. During the inference stage, this network part is detached to meet a real-time requirement as well as to ensure the tracking accuracy. In addition, the impacts of the quantity setting of the input point cloud and re-initiation strategy are discussed; these are significant to the performance but have been ignored by former works. The experimental results show that the proposed method has a distinct improvement even if the tracked object is sparse and small scale.
This paper presents a local structural feature description of point cloud to efficiently extract local geometric and structure features from LIDAR data for 3-dimensional objective. This approach using hierarchical projection to maps neighbor points with different radial distance to multi-Mercator layers to obtain different distance information of neighbor points to key points. The Mercator projection, a conformal mapping method, the preserves geometric and structure relationship properly. The local features of key points can be obtained by calculating the distribution histogram of each Mercator planes with normalization method. Comparing the proposed approach with other hand-crafted feature extraction methods on Stanford Bologna dataset and 3Dmatch dataset, our methods outperform on descriptiveness, robustness to noise.
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