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.
arXiv preprint arXiv:2201.12296 (2022)
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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.
MAMVI performs unified single-step TTA on masked multi-view point clouds with hybrid masking and confidence-adaptive learning rates, reporting SOTA on ShapeNet-C and ScanObjectNN-C plus 4.9-8.9x speedup.
A survey that categorizes deep learning models for point cloud tasks by backbone architecture, evaluates benchmark performance, and outlines challenges and future research directions.
citing papers explorer
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PointQ-Bench: Benchmarking Diagnostic and Interpretable Point Cloud Quality Assessment
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: A Learnable Integral Transform That Subsumes Convolution, Attention, and Recurrence
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.
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MAMVI: 3D Test-Time Adaptation via Masked Multi-View Point Clouds
MAMVI performs unified single-step TTA on masked multi-view point clouds with hybrid masking and confidence-adaptive learning rates, reporting SOTA on ShapeNet-C and ScanObjectNN-C plus 4.9-8.9x speedup.
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A Survey on Deep Learning Architectures for Point Cloud Classification and Segmentation
A survey that categorizes deep learning models for point cloud tasks by backbone architecture, evaluates benchmark performance, and outlines challenges and future research directions.