PointQ-Bench is a benchmark with annotated point clouds supporting anomaly sensing, defect diagnosis, usability grading, and open-ended quality reporting, plus the SSFRQ-5D evaluation protocol.
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ITNet frames convolution, attention, and recurrence as special cases of one learnable integral transform with an MLP kernel and shows a single shared operator plus modality encoders matches specialized models on ImageNet-1K, GLUE, ModelNet40, VQA v2, and NLVR2.
A survey that categorizes deep learning models for point cloud tasks by backbone architecture, evaluates benchmark performance, and outlines challenges and future research directions.
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