prompts stringlengths 81 413 | metrics_response stringlengths 0 371 |
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What metrics were used to measure the PPFNet model in the PPFNet: Global Context Aware Local Features for Robust 3D Point Matching paper on the 3DMatch Benchmark dataset? | Feature Matching Recall |
What metrics were used to measure the FPFH + RANSAC model in the Fast Point Feature Histograms (FPFH) for 3D Registration paper on the 3DMatch Benchmark dataset? | Feature Matching Recall |
What metrics were used to measure the Greedy Grid Search model in the Challenging the Universal Representation of Deep Models for 3D Point Cloud Registration paper on the FAUST-partial (60%+ overlap, Rot 0-45, Trans -50-50) dataset? | Recall (%), RRE (degrees), RTE(cm) |
What metrics were used to measure the GeoTransformer model in the Geometric Transformer for Fast and Robust Point Cloud Registration paper on the FAUST-partial (60%+ overlap, Rot 0-45, Trans -50-50) dataset? | Recall (%), RRE (degrees), RTE(cm) |
What metrics were used to measure the DIP model in the Distinctive 3D local deep descriptors paper on the FAUST-partial (60%+ overlap, Rot 0-45, Trans -50-50) dataset? | Recall (%), RRE (degrees), RTE(cm) |
What metrics were used to measure the FCGF + PointDSC model in the PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency paper on the FAUST-partial (60%+ overlap, Rot 0-45, Trans -50-50) dataset? | Recall (%), RRE (degrees), RTE(cm) |
What metrics were used to measure the SpinNet model in the SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration paper on the FAUST-partial (60%+ overlap, Rot 0-45, Trans -50-50) dataset? | Recall (%), RRE (degrees), RTE(cm) |
What metrics were used to measure the FCGF + YOHO-C model in the You Only Hypothesize Once: Point Cloud Registration with Rotation-equivariant Descriptors paper on the FAUST-partial (60%+ overlap, Rot 0-45, Trans -50-50) dataset? | Recall (%), RRE (degrees), RTE(cm) |
What metrics were used to measure the FCGF + YOHO-O model in the You Only Hypothesize Once: Point Cloud Registration with Rotation-equivariant Descriptors paper on the FAUST-partial (60%+ overlap, Rot 0-45, Trans -50-50) dataset? | Recall (%), RRE (degrees), RTE(cm) |
What metrics were used to measure the FPFH-8M model in the Fast Point Feature Histograms (FPFH) for 3D Registration paper on the FAUST-partial (60%+ overlap, Rot 0-45, Trans -50-50) dataset? | Recall (%), RRE (degrees), RTE(cm) |
What metrics were used to measure the NgeNet model in the Leveraging Inlier Correspondences Proportion for Point Cloud Registration paper on the 3DMatch (at least 30% overlapped - sample 5k interest points) dataset? | Recall ( correspondence RMSE below 0.2) |
What metrics were used to measure the REGTR model in the REGTR: End-to-end Point Cloud Correspondences with Transformers paper on the 3DMatch (at least 30% overlapped - sample 5k interest points) dataset? | Recall ( correspondence RMSE below 0.2) |
What metrics were used to measure the Predator-1k model in the PREDATOR: Registration of 3D Point Clouds with Low Overlap paper on the 3DMatch (at least 30% overlapped - sample 5k interest points) dataset? | Recall ( correspondence RMSE below 0.2) |
What metrics were used to measure the Predator-5k model in the PREDATOR: Registration of 3D Point Clouds with Low Overlap paper on the 3DMatch (at least 30% overlapped - sample 5k interest points) dataset? | Recall ( correspondence RMSE below 0.2) |
What metrics were used to measure the PCAM (reported in REGTR) model in the PCAM: Product of Cross-Attention Matrices for Rigid Registration of Point Clouds paper on the 3DMatch (at least 30% overlapped - sample 5k interest points) dataset? | Recall ( correspondence RMSE below 0.2) |
What metrics were used to measure the DGR (reported in REGTR) model in the Deep Global Registration paper on the 3DMatch (at least 30% overlapped - sample 5k interest points) dataset? | Recall ( correspondence RMSE below 0.2) |
What metrics were used to measure the FCGF (reported in PREDATOR) model in the Fully Convolutional Geometric Features paper on the 3DMatch (at least 30% overlapped - sample 5k interest points) dataset? | Recall ( correspondence RMSE below 0.2) |
What metrics were used to measure the D3Feat (reported in PREDATOR) model in the D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features paper on the 3DMatch (at least 30% overlapped - sample 5k interest points) dataset? | Recall ( correspondence RMSE below 0.2) |
What metrics were used to measure the 3DSN (reported in PREDATOR) model in the The Perfect Match: 3D Point Cloud Matching with Smoothed Densities paper on the 3DMatch (at least 30% overlapped - sample 5k interest points) dataset? | Recall ( correspondence RMSE below 0.2) |
What metrics were used to measure the Predator-NR model in the PREDATOR: Registration of 3D Point Clouds with Low Overlap paper on the 3DMatch (at least 30% overlapped - sample 5k interest points) dataset? | Recall ( correspondence RMSE below 0.2) |
What metrics were used to measure the OMNet (reported in REGTR) model in the OMNet: Learning Overlapping Mask for Partial-to-Partial Point Cloud Registration paper on the 3DMatch (at least 30% overlapped - sample 5k interest points) dataset? | Recall ( correspondence RMSE below 0.2) |
What metrics were used to measure the GeoTransformer - P2PNet model in the Geometric Transformer for Fast and Robust Point Cloud Registration paper on the 3DLoMatch (10-30% overlap) dataset? | Recall ( correspondence RMSE below 0.2) |
What metrics were used to measure the NgeNet model in the Leveraging Inlier Correspondences Proportion for Point Cloud Registration paper on the 3DLoMatch (10-30% overlap) dataset? | Recall ( correspondence RMSE below 0.2) |
What metrics were used to measure the REGTR model in the REGTR: End-to-end Point Cloud Correspondences with Transformers paper on the 3DLoMatch (10-30% overlap) dataset? | Recall ( correspondence RMSE below 0.2) |
What metrics were used to measure the Predator-1k model in the PREDATOR: Registration of 3D Point Clouds with Low Overlap paper on the 3DLoMatch (10-30% overlap) dataset? | Recall ( correspondence RMSE below 0.2) |
What metrics were used to measure the Predator-5k model in the PREDATOR: Registration of 3D Point Clouds with Low Overlap paper on the 3DLoMatch (10-30% overlap) dataset? | Recall ( correspondence RMSE below 0.2) |
What metrics were used to measure the PCAM (reported in REGTR) model in the PCAM: Product of Cross-Attention Matrices for Rigid Registration of Point Clouds paper on the 3DLoMatch (10-30% overlap) dataset? | Recall ( correspondence RMSE below 0.2) |
What metrics were used to measure the DGR (reported in REGTR) model in the Deep Global Registration paper on the 3DLoMatch (10-30% overlap) dataset? | Recall ( correspondence RMSE below 0.2) |
What metrics were used to measure the FCGF (reported in PREDATOR) model in the Fully Convolutional Geometric Features paper on the 3DLoMatch (10-30% overlap) dataset? | Recall ( correspondence RMSE below 0.2) |
What metrics were used to measure the D3Feat (reported in PREDATOR) model in the D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features paper on the 3DLoMatch (10-30% overlap) dataset? | Recall ( correspondence RMSE below 0.2) |
What metrics were used to measure the 3DSN (reported in PREDATOR) model in the The Perfect Match: 3D Point Cloud Matching with Smoothed Densities paper on the 3DLoMatch (10-30% overlap) dataset? | Recall ( correspondence RMSE below 0.2) |
What metrics were used to measure the Predator-NR model in the PREDATOR: Registration of 3D Point Clouds with Low Overlap paper on the 3DLoMatch (10-30% overlap) dataset? | Recall ( correspondence RMSE below 0.2) |
What metrics were used to measure the OMNet (reported in REGTR) model in the OMNet: Learning Overlapping Mask for Partial-to-Partial Point Cloud Registration paper on the 3DLoMatch (10-30% overlap) dataset? | Recall ( correspondence RMSE below 0.2) |
What metrics were used to measure the Greedy Grid Search model in the Challenging the Universal Representation of Deep Models for 3D Point Cloud Registration paper on the FAUST-partial (60%+ overlap, Rot 0-45, Trans -50-50, trained on 3DMatch) dataset? | Recall (%), RRE (degrees), RTE (cm) |
What metrics were used to measure the GeoTransformer model in the Geometric Transformer for Fast and Robust Point Cloud Registration paper on the FAUST-partial (60%+ overlap, Rot 0-45, Trans -50-50, trained on 3DMatch) dataset? | Recall (%), RRE (degrees), RTE (cm) |
What metrics were used to measure the DIP model in the Distinctive 3D local deep descriptors paper on the FAUST-partial (60%+ overlap, Rot 0-45, Trans -50-50, trained on 3DMatch) dataset? | Recall (%), RRE (degrees), RTE (cm) |
What metrics were used to measure the FCGF + PointDSC model in the PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency paper on the FAUST-partial (60%+ overlap, Rot 0-45, Trans -50-50, trained on 3DMatch) dataset? | Recall (%), RRE (degrees), RTE (cm) |
What metrics were used to measure the SpinNet model in the SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration paper on the FAUST-partial (60%+ overlap, Rot 0-45, Trans -50-50, trained on 3DMatch) dataset? | Recall (%), RRE (degrees), RTE (cm) |
What metrics were used to measure the FCGF + YOHO-C model in the You Only Hypothesize Once: Point Cloud Registration with Rotation-equivariant Descriptors paper on the FAUST-partial (60%+ overlap, Rot 0-45, Trans -50-50, trained on 3DMatch) dataset? | Recall (%), RRE (degrees), RTE (cm) |
What metrics were used to measure the FCGF + YOHO-O model in the You Only Hypothesize Once: Point Cloud Registration with Rotation-equivariant Descriptors paper on the FAUST-partial (60%+ overlap, Rot 0-45, Trans -50-50, trained on 3DMatch) dataset? | Recall (%), RRE (degrees), RTE (cm) |
What metrics were used to measure the FPFH-8M model in the Fast Point Feature Histograms (FPFH) for 3D Registration paper on the FAUST-partial (60%+ overlap, Rot 0-45, Trans -50-50, trained on 3DMatch) dataset? | Recall (%), RRE (degrees), RTE (cm) |
What metrics were used to measure the GeDi model in the Learning general and distinctive 3D local deep descriptors for point cloud registration paper on the ETH (trained on 3DMatch) dataset? | Recall |
What metrics were used to measure the DIP model in the Distinctive 3D local deep descriptors paper on the ETH (trained on 3DMatch) dataset? | Recall |
What metrics were used to measure the SpinNet model in the SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration paper on the ETH (trained on 3DMatch) dataset? | Recall |
What metrics were used to measure the PerfectMatch model in the The Perfect Match: 3D Point Cloud Matching with Smoothed Densities paper on the ETH (trained on 3DMatch) dataset? | Recall |
What metrics were used to measure the Greedy Grid Search model in the Challenging the Universal Representation of Deep Models for 3D Point Cloud Registration paper on the ETH (trained on 3DMatch) dataset? | Recall |
What metrics were used to measure the LMVD model in the End-to-End Learning Local Multi-view Descriptors for 3D Point Clouds paper on the ETH (trained on 3DMatch) dataset? | Recall |
What metrics were used to measure the D3Feat-pred model in the D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features paper on the ETH (trained on 3DMatch) dataset? | Recall |
What metrics were used to measure the FPFH model in the Fast Point Feature Histograms (FPFH) for 3D Registration paper on the ETH (trained on 3DMatch) dataset? | Recall |
What metrics were used to measure the CGF model in the Learning Compact Geometric Features paper on the ETH (trained on 3DMatch) dataset? | Recall |
What metrics were used to measure the 3DMatch model in the 3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions paper on the ETH (trained on 3DMatch) dataset? | Recall |
What metrics were used to measure the FCGF model in the Fully Convolutional Geometric Features paper on the ETH (trained on 3DMatch) dataset? | Recall |
What metrics were used to measure the GeDi model in the Learning general and distinctive 3D local deep descriptors for point cloud registration paper on the KITTI dataset? | Success Rate |
What metrics were used to measure the D3Feat-pred model in the D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features paper on the KITTI dataset? | Success Rate |
What metrics were used to measure the SpinNet model in the SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration paper on the KITTI dataset? | Success Rate |
What metrics were used to measure the DIP model in the Distinctive 3D local deep descriptors paper on the KITTI dataset? | Success Rate |
What metrics were used to measure the FCGF model in the Fully Convolutional Geometric Features paper on the KITTI dataset? | Success Rate |
What metrics were used to measure the 3DFeat-Net model in the 3DFeat-Net: Weakly Supervised Local 3D Features for Point Cloud Registration paper on the KITTI dataset? | Success Rate |
What metrics were used to measure the GeDi model in the Learning general and distinctive 3D local deep descriptors for point cloud registration paper on the KITTI (trained on 3DMatch) dataset? | Success Rate |
What metrics were used to measure the DIP model in the Distinctive 3D local deep descriptors paper on the KITTI (trained on 3DMatch) dataset? | Success Rate |
What metrics were used to measure the Greedy Grid Search model in the Challenging the Universal Representation of Deep Models for 3D Point Cloud Registration paper on the KITTI (trained on 3DMatch) dataset? | Success Rate |
What metrics were used to measure the SpinNet model in the SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration paper on the KITTI (trained on 3DMatch) dataset? | Success Rate |
What metrics were used to measure the D3Feat-pred model in the D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features paper on the KITTI (trained on 3DMatch) dataset? | Success Rate |
What metrics were used to measure the FCGF model in the Fully Convolutional Geometric Features paper on the KITTI (trained on 3DMatch) dataset? | Success Rate |
What metrics were used to measure the Recursive Refinement Network model in the Recursive Refinement Network for Deformable Lung Registration between Exhale and Inhale CT Scans paper on the DIR-LAB COPDgene dataset? | landmarks |
What metrics were used to measure the LKRetina model in the Reverse Knowledge Distillation: Training a Large Model using a Small One for Retinal Image Matching on Limited Data paper on the FIRE dataset? | mAUC |
What metrics were used to measure the SuperRetina model in the Semi-Supervised Keypoint Detector and Descriptor for Retinal Image Matching paper on the FIRE dataset? | mAUC |
What metrics were used to measure the REMPE, JBHI 2020 model in the Semi-Supervised Keypoint Detector and Descriptor for Retinal Image Matching paper on the FIRE dataset? | mAUC |
What metrics were used to measure the GLAMpoints, ICCV 2019 model in the GLAMpoints: Greedily Learned Accurate Match points paper on the FIRE dataset? | mAUC |
What metrics were used to measure the SIFT model in the Distinctive Image Features from Scale-Invariant Keypoints paper on the FIRE dataset? | mAUC |
What metrics were used to measure the PBO, ICIP 2010 model in the Semi-Supervised Keypoint Detector and Descriptor for Retinal Image Matching paper on the FIRE dataset? | mAUC |
What metrics were used to measure the Region-specific Diffeomorphic Metric Mapping model in the Region-specific Diffeomorphic Metric Mapping paper on the Osteoarthritis Initiative dataset? | Dice |
What metrics were used to measure the vSVF-net [shen2019networks] model in the Networks for Joint Affine and Non-parametric Image Registration paper on the Osteoarthritis Initiative dataset? | Dice |
What metrics were used to measure the CAV-MAE (Audio-Visual) model in the Contrastive Audio-Visual Masked Autoencoder paper on the AudioSet dataset? | mean average precision |
What metrics were used to measure the mn40_as (Ensemble) model in the Efficient Large-scale Audio Tagging via Transformer-to-CNN Knowledge Distillation paper on the AudioSet dataset? | mean average precision |
What metrics were used to measure the PaSST model in the Efficient Training of Audio Transformers with Patchout paper on the AudioSet dataset? | mean average precision |
What metrics were used to measure the DyMN-L (Audio-Only, Single) model in the Dynamic Convolutional Neural Networks as Efficient Pre-trained Audio Models paper on the AudioSet dataset? | mean average precision |
What metrics were used to measure the Audio Spectrogram Transformer model in the AST: Audio Spectrogram Transformer paper on the AudioSet dataset? | mean average precision |
What metrics were used to measure the mn40_as (Single) model in the Efficient Large-scale Audio Tagging via Transformer-to-CNN Knowledge Distillation paper on the AudioSet dataset? | mean average precision |
What metrics were used to measure the PSLA model in the PSLA: Improving Audio Tagging with Pretraining, Sampling, Labeling, and Aggregation paper on the AudioSet dataset? | mean average precision |
What metrics were used to measure the ST-SED model in the Zero-shot Audio Source Separation through Query-based Learning from Weakly-labeled Data paper on the AudioSet dataset? | mean average precision |
What metrics were used to measure the CAV-MAE (Audio-Only) model in the Contrastive Audio-Visual Masked Autoencoder paper on the AudioSet dataset? | mean average precision |
What metrics were used to measure the ERANN-1-6 model in the ERANNs: Efficient Residual Audio Neural Networks for Audio Pattern Recognition paper on the AudioSet dataset? | mean average precision |
What metrics were used to measure the CNN14 model in the PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition paper on the AudioSet dataset? | mean average precision |
What metrics were used to measure the 2DCNN + BiLSTM + ResNet + MLF model in the Audio-Visual Speech and Gesture Recognition by Sensors of Mobile Devices paper on the LRW dataset? | Top-1 Accuracy |
What metrics were used to measure the CTC/Attention model in the Auto-AVSR: Audio-Visual Speech Recognition with Automatic Labels paper on the LRS2 dataset? | Test WER |
What metrics were used to measure the MoCo + wav2vec (w/o extLM) model in the Leveraging Unimodal Self-Supervised Learning for Multimodal Audio-Visual Speech Recognition paper on the LRS2 dataset? | Test WER |
What metrics were used to measure the End2end Conformer model in the End-to-end Audio-visual Speech Recognition with Conformers paper on the LRS2 dataset? | Test WER |
What metrics were used to measure the LF-MMI TDNN model in the Audio-visual Recognition of Overlapped speech for the LRS2 dataset paper on the LRS2 dataset? | Test WER |
What metrics were used to measure the CTC/Attention model in the Audio-Visual Speech Recognition With A Hybrid CTC/Attention Architecture paper on the LRS2 dataset? | Test WER |
What metrics were used to measure the TM-CTC model in the Deep Audio-Visual Speech Recognition paper on the LRS2 dataset? | Test WER |
What metrics were used to measure the TM-Seq2seq model in the Deep Audio-Visual Speech Recognition paper on the LRS2 dataset? | Test WER |
What metrics were used to measure the CTC/Attention model in the Auto-AVSR: Audio-Visual Speech Recognition with Automatic Labels paper on the LRS3-TED dataset? | Word Error Rate (WER) |
What metrics were used to measure the AV-HuBERT Large model in the Robust Self-Supervised Audio-Visual Speech Recognition paper on the LRS3-TED dataset? | Word Error Rate (WER) |
What metrics were used to measure the RAVEn Large model in the Jointly Learning Visual and Auditory Speech Representations from Raw Data paper on the LRS3-TED dataset? | Word Error Rate (WER) |
What metrics were used to measure the Hyb-Conformer model in the End-to-end Audio-visual Speech Recognition with Conformers paper on the LRS3-TED dataset? | Word Error Rate (WER) |
What metrics were used to measure the RNN-T model in the Recurrent Neural Network Transducer for Audio-Visual Speech Recognition paper on the LRS3-TED dataset? | Word Error Rate (WER) |
What metrics were used to measure the EG-seq2seq model in the Discriminative Multi-modality Speech Recognition paper on the LRS3-TED dataset? | Word Error Rate (WER) |
What metrics were used to measure the TM-seq2seq model in the Deep Audio-Visual Speech Recognition paper on the LRS3-TED dataset? | Word Error Rate (WER) |
What metrics were used to measure the CNN + 1D CNN model in the Improved Bengali Image Captioning via deep convolutional neural network based encoder-decoder model paper on the BanglaLekhaImageCaptions dataset? | BLEU-1, BLEU-2, BLEU-3, BLEU-4, CIDEr, METEOR, ROUGE-L, SPICE |
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