task_path stringlengths 3 199 ⌀ | dataset stringlengths 1 128 ⌀ | model_name stringlengths 1 223 ⌀ | paper_url stringlengths 21 601 ⌀ | metric_name stringlengths 1 50 ⌀ | metric_value stringlengths 1 9.22k ⌀ |
|---|---|---|---|---|---|
16k > Object Detection > 3D Object Detection | DAIR-V2X-I | ImVoxelNet | https://arxiv.org/abs/2106.01178v3 | AP|R40(hard) | 37.6 |
16k > Object Detection > 3D Object Detection | Spiideo SoccerNet SynLoc | Baseline-960x960 | https://www.scitepress.org/Papers/2025/131082/ | mAP-LocSim | 79.3 |
16k > Object Detection > 3D Object Detection | Spiideo SoccerNet SynLoc | Baseline-960x960 | https://www.scitepress.org/Papers/2025/131082/ | F1 | 89.0 |
16k > Object Detection > 3D Object Detection | Spiideo SoccerNet SynLoc | Baseline-960x960 | https://www.scitepress.org/Papers/2025/131082/ | FrameAccuracy | 31.6 |
16k > Object Detection > 3D Object Detection | Spiideo SoccerNet SynLoc | Baseline-640x640 | https://www.scitepress.org/Papers/2025/131082/ | mAP-LocSim | 67.8 |
16k > Object Detection > 3D Object Detection | Spiideo SoccerNet SynLoc | Baseline-640x640 | https://www.scitepress.org/Papers/2025/131082/ | F1 | 83.6 |
16k > Object Detection > 3D Object Detection | Spiideo SoccerNet SynLoc | Baseline-640x640 | https://www.scitepress.org/Papers/2025/131082/ | FrameAccuracy | 15.4 |
16k > Object Detection > 3D Object Detection | KITTI Cyclist Hard val | M3DeTR | https://arxiv.org/abs/2104.11896v3 | AP | 68.29 |
16k > Object Detection > 3D Object Detection | KITTI Cyclist Hard val | PVCNN | https://arxiv.org/abs/1907.03739v2 | AP | 56.24 |
16k > Object Detection > 3D Object Detection | KITTI Cyclist Hard val | F-PointNet++ [Qi:2018fd] | http://arxiv.org/abs/1711.08488v2 | AP | 53.37 |
16k > Object Detection > 3D Object Detection | KITTI Cyclist Hard val | F-PointNet [Qi:2018fd] | http://arxiv.org/abs/1711.08488v2 | AP | 52.65 |
16k > Object Detection > 3D Object Detection | KITTI Pedestrian Hard | PiFeNet | https://arxiv.org/abs/2112.15458v3 | Average Precision | 0.4271 |
16k > Object Detection > 3D Object Detection | DAIR-V2X | CoBEVFlow | null | AP50 | 80.7 |
16k > Object Detection > 3D Object Detection | DAIR-V2X | Where2comm | https://arxiv.org/abs/2209.12836v1 | AP50 | 63.71 |
16k > Object Detection > 3D Object Detection | KITTI Pedestrian Moderate val | PVCNN | https://arxiv.org/abs/1907.03739v2 | AP | 64.71 |
16k > Object Detection > 3D Object Detection | KITTI Pedestrian Moderate val | F-PointNet++ [Qi:2018fd] | http://arxiv.org/abs/1711.08488v2 | AP | 61.32 |
16k > Object Detection > 3D Object Detection | KITTI Pedestrian Moderate val | M3DeTR | https://arxiv.org/abs/2104.11896v3 | AP | 60.63 |
16k > Object Detection > 3D Object Detection | KITTI Pedestrian Moderate val | F-PointNet [Qi:2018fd] | http://arxiv.org/abs/1711.08488v2 | AP | 55.85 |
16k > Object Detection > 3D Object Detection | aiMotive Dataset | Lidar-Radar-Camera | https://arxiv.org/abs/2211.09445v3 | BEV AP@0.3 Highway | 0.762 |
16k > Object Detection > 3D Object Detection | aiMotive Dataset | Lidar-Radar-Camera | https://arxiv.org/abs/2211.09445v3 | BEV AP@0.3 Urban | 0.644 |
16k > Object Detection > 3D Object Detection | aiMotive Dataset | Lidar-Radar-Camera | https://arxiv.org/abs/2211.09445v3 | BEV AP@0.3 Night | 0.730 |
16k > Object Detection > 3D Object Detection | aiMotive Dataset | Lidar-Radar-Camera | https://arxiv.org/abs/2211.09445v3 | BEV AP@0.3 Rain | 0.423 |
16k > Object Detection > 3D Object Detection | aiMotive Dataset | Lidar | https://arxiv.org/abs/2211.09445v3 | BEV AP@0.3 Highway | 0.757 |
16k > Object Detection > 3D Object Detection | aiMotive Dataset | Lidar | https://arxiv.org/abs/2211.09445v3 | BEV AP@0.3 Urban | 0.630 |
16k > Object Detection > 3D Object Detection | aiMotive Dataset | Lidar | https://arxiv.org/abs/2211.09445v3 | BEV AP@0.3 Night | 0.754 |
16k > Object Detection > 3D Object Detection | aiMotive Dataset | Lidar | https://arxiv.org/abs/2211.09445v3 | BEV AP@0.3 Rain | 0.568 |
16k > Object Detection > 3D Object Detection | aiMotive Dataset | Lidar-Radar | https://arxiv.org/abs/2211.09445v3 | BEV AP@0.3 Highway | 0.741 |
16k > Object Detection > 3D Object Detection | aiMotive Dataset | Lidar-Radar | https://arxiv.org/abs/2211.09445v3 | BEV AP@0.3 Urban | 0.638 |
16k > Object Detection > 3D Object Detection | aiMotive Dataset | Lidar-Radar | https://arxiv.org/abs/2211.09445v3 | BEV AP@0.3 Night | 0.766 |
16k > Object Detection > 3D Object Detection | aiMotive Dataset | Lidar-Radar | https://arxiv.org/abs/2211.09445v3 | BEV AP@0.3 Rain | 0.517 |
16k > Object Detection > 3D Object Detection | 3RScan | UniDet3D | https://arxiv.org/abs/2409.04234v1 | mAP@0.25 | 64.7 |
16k > Object Detection > 3D Object Detection | 3RScan | UniDet3D | https://arxiv.org/abs/2409.04234v1 | mAP@0.5 | 48.6 |
16k > Object Detection > 3D Object Detection | 3RScan | TR3D | https://arxiv.org/abs/2302.02858v3 | mAP@0.25 | 62.3 |
16k > Object Detection > 3D Object Detection | 3RScan | TR3D | https://arxiv.org/abs/2302.02858v3 | mAP@0.5 | 45.4 |
16k > Object Detection > 3D Object Detection | 3RScan | FCAF3D | https://arxiv.org/abs/2112.00322v2 | mAP@0.25 | 60.1 |
16k > Object Detection > 3D Object Detection | 3RScan | FCAF3D | https://arxiv.org/abs/2112.00322v2 | mAP@0.5 | 42.6 |
16k > Object Detection > 3D Object Detection | KITTI Cars Hard | TRTConv | null | AP | 80.38 % |
16k > Object Detection > 3D Object Detection | KITTI Cars Hard | 3D Dual-Fusion | https://arxiv.org/abs/2211.13529v2 | AP | 79.39% |
16k > Object Detection > 3D Object Detection | KITTI Cars Hard | GLENet-VR | https://arxiv.org/abs/2207.02466v5 | AP | 78.43% |
16k > Object Detection > 3D Object Detection | KITTI Cars Hard | PV-RCNN++ | https://arxiv.org/abs/2102.00463v3 | AP | 77.15% |
16k > Object Detection > 3D Object Detection | KITTI Cars Hard | SE-SSD | https://arxiv.org/abs/2104.09804v1 | AP | 77.15% |
16k > Object Detection > 3D Object Detection | KITTI Cars Hard | Voxel R-CNN | https://arxiv.org/abs/2012.15712v2 | AP | 77.06 |
16k > Object Detection > 3D Object Detection | KITTI Cars Hard | M3DeTR | https://arxiv.org/abs/2104.11896v3 | AP | 76.96% |
16k > Object Detection > 3D Object Detection | KITTI Cars Hard | PV-RCNN | https://arxiv.org/abs/1912.13192v2 | AP | 76.82% |
16k > Object Detection > 3D Object Detection | KITTI Cars Hard | STD | https://arxiv.org/abs/1907.10471v1 | AP | 76.06% |
16k > Object Detection > 3D Object Detection | KITTI Cars Hard | PC-RGNN | https://arxiv.org/abs/2012.10412v3 | AP | 75.54% |
16k > Object Detection > 3D Object Detection | KITTI Cars Hard | SVGA-Net | https://arxiv.org/abs/2006.04043v2 | AP | 74.63% |
16k > Object Detection > 3D Object Detection | KITTI Cars Hard | Joint | http://openaccess.thecvf.com/content_CVPR_2020/html/Zhou_Joint_3D_Instance_Segmentation_and_Object_Detection_for_Autonomous_Driving_CVPR_2020_paper.html | AP | 74.30% |
16k > Object Detection > 3D Object Detection | KITTI Cars Hard | CIA-SSD | https://arxiv.org/abs/2012.03015v1 | AP | 72.87 |
16k > Object Detection > 3D Object Detection | KITTI Cars Hard | SA-SSD+EBM | https://arxiv.org/abs/2012.04634v2 | AP | 72.78% |
16k > Object Detection > 3D Object Detection | KITTI Cars Hard | PointRGCN | https://arxiv.org/abs/1911.12236v1 | AP | 70.60% |
16k > Object Detection > 3D Object Detection | KITTI Cars Hard | UberATG-MMF | https://arxiv.org/abs/2012.12397v1 | AP | 68.41% |
16k > Object Detection > 3D Object Detection | KITTI Cars Hard | F-ConvNet | https://arxiv.org/abs/1903.01864v2 | AP | 68.08% |
16k > Object Detection > 3D Object Detection | KITTI Cars Hard | PointRCNN | https://arxiv.org/abs/1812.04244v2 | AP | 67.86% |
16k > Object Detection > 3D Object Detection | KITTI Cars Hard | AVOD + Feature Pyramid | http://arxiv.org/abs/1712.02294v4 | AP | 66.38% |
16k > Object Detection > 3D Object Detection | KITTI Cars Hard | IPOD | http://arxiv.org/abs/1812.05276v1 | AP | 66.33% |
16k > Object Detection > 3D Object Detection | KITTI Cars Hard | PC-CNN-V2 | http://arxiv.org/abs/1803.00387v1 | AP | 64.83% |
16k > Object Detection > 3D Object Detection | KITTI Cars Hard | Frustum PointNets | http://arxiv.org/abs/1711.08488v2 | AP | 62.19% |
16k > Object Detection > 3D Object Detection | KITTI Cars Hard | RoarNet | http://arxiv.org/abs/1811.03818v1 | AP | 59.16% |
16k > Object Detection > 3D Object Detection | KITTI Cars Hard | VoxelNet | http://arxiv.org/abs/1711.06396v1 | AP | 57.73% |
16k > Object Detection > 3D Object Detection | KITTI Cars Hard | PGD | https://arxiv.org/abs/2107.14160v3 | AP | 9.39% |
16k > Object Detection > 3D Object Detection | V2XSet | V2X-ViT | https://arxiv.org/abs/2203.10638v3 | AP0.5 (Perfect) | 0.882 |
16k > Object Detection > 3D Object Detection | V2XSet | V2X-ViT | https://arxiv.org/abs/2203.10638v3 | AP0.7 (Perfect) | 0.712 |
16k > Object Detection > 3D Object Detection | V2XSet | V2X-ViT | https://arxiv.org/abs/2203.10638v3 | AP0.5 (Noisy) | 0.836 |
16k > Object Detection > 3D Object Detection | V2XSet | V2X-ViT | https://arxiv.org/abs/2203.10638v3 | AP0.7 (Noisy) | 0.614 |
16k > Object Detection > 3D Object Detection | V2XSet | V2X-AHD | https://arxiv.org/abs/2310.06603v1 | AP0.5 (Perfect) | 0.855 |
16k > Object Detection > 3D Object Detection | V2XSet | V2X-AHD | https://arxiv.org/abs/2310.06603v1 | AP0.7 (Perfect) | 0.724 |
16k > Object Detection > 3D Object Detection | V2XSet | V2VNet | https://arxiv.org/abs/2008.07519v1 | AP0.5 (Perfect) | 0.845 |
16k > Object Detection > 3D Object Detection | V2XSet | V2VNet | https://arxiv.org/abs/2008.07519v1 | AP0.7 (Perfect) | 0.677 |
16k > Object Detection > 3D Object Detection | V2XSet | V2VNet | https://arxiv.org/abs/2008.07519v1 | AP0.5 (Noisy) | 0.791 |
16k > Object Detection > 3D Object Detection | V2XSet | V2VNet | https://arxiv.org/abs/2008.07519v1 | AP0.7 (Noisy) | 0.493 |
16k > Object Detection > 3D Object Detection | V2XSet | DiscoNet | https://arxiv.org/abs/2111.00643v2 | AP0.5 (Perfect) | 0.844 |
16k > Object Detection > 3D Object Detection | V2XSet | DiscoNet | https://arxiv.org/abs/2111.00643v2 | AP0.7 (Perfect) | 0.695 |
16k > Object Detection > 3D Object Detection | V2XSet | DiscoNet | https://arxiv.org/abs/2111.00643v2 | AP0.5 (Noisy) | 0.798 |
16k > Object Detection > 3D Object Detection | V2XSet | DiscoNet | https://arxiv.org/abs/2111.00643v2 | AP0.7 (Noisy) | 0.541 |
16k > Object Detection > 3D Object Detection | V2XSet | F-Cooper | https://arxiv.org/abs/1909.06459v1 | AP0.5 (Perfect) | 0.840 |
16k > Object Detection > 3D Object Detection | V2XSet | F-Cooper | https://arxiv.org/abs/1909.06459v1 | AP0.7 (Perfect) | 0.680 |
16k > Object Detection > 3D Object Detection | V2XSet | F-Cooper | https://arxiv.org/abs/1909.06459v1 | AP0.5 (Noisy) | 0.715 |
16k > Object Detection > 3D Object Detection | V2XSet | F-Cooper | https://arxiv.org/abs/1909.06459v1 | AP0.7 (Noisy) | 0.469 |
16k > Object Detection > 3D Object Detection | V2XSet | AttentiveFusion | https://arxiv.org/abs/2109.07644v5 | AP0.5 (Perfect) | 0.807 |
16k > Object Detection > 3D Object Detection | V2XSet | AttentiveFusion | https://arxiv.org/abs/2109.07644v5 | AP0.7 (Perfect) | 0.664 |
16k > Object Detection > 3D Object Detection | V2XSet | AttentiveFusion | https://arxiv.org/abs/2109.07644v5 | AP0.5 (Noisy) | 0.709 |
16k > Object Detection > 3D Object Detection | V2XSet | AttentiveFusion | https://arxiv.org/abs/2109.07644v5 | AP0.7 (Noisy) | 0.487 |
16k > Object Detection > 3D Object Detection | ScanNet++ | UniDet3D | https://arxiv.org/abs/2409.04234v1 | mAP@0.25 | 26.4 |
16k > Object Detection > 3D Object Detection | ScanNet++ | UniDet3D | https://arxiv.org/abs/2409.04234v1 | mAP@0.5 | 17.2 |
16k > Object Detection > 3D Object Detection | ScanNet++ | TR3D | https://arxiv.org/abs/2302.02858v3 | mAP@0.25 | 26.2 |
16k > Object Detection > 3D Object Detection | ScanNet++ | TR3D | https://arxiv.org/abs/2302.02858v3 | mAP@0.5 | 14.5 |
16k > Object Detection > 3D Object Detection | ScanNet++ | FCAF3D | https://arxiv.org/abs/2112.00322v2 | mAP@0.25 | 22.3 |
16k > Object Detection > 3D Object Detection | ScanNet++ | FCAF3D | https://arxiv.org/abs/2112.00322v2 | mAP@0.5 | 11.4 |
16k > Object Detection > 3D Object Detection | 3D Object Detection on Argoverse2 Camera Only | Far3D | https://arxiv.org/abs/2308.09616v2 | Average mAP | 24.4 |
16k > Object Detection > 3D Object Detection | 3D Object Detection on Argoverse2 Camera Only | StreamPETR | https://arxiv.org/abs/2303.11926v2 | Average mAP | 20.3 |
16k > Object Detection > 3D Object Detection | 3D Object Detection on Argoverse2 Camera Only | PETR | https://arxiv.org/abs/2203.05625v3 | Average mAP | 17.6 |
16k > Object Detection > 3D Object Detection | DTTD-Mobile | DTTDNet | https://arxiv.org/abs/2309.13570v4 | ADD AUC | 73.99 |
16k > Object Detection > 3D Object Detection | DTTD-Mobile | DTTDNet | https://arxiv.org/abs/2309.13570v4 | ADD-S AUC | 88.10 |
16k > Object Detection > 3D Object Detection | DTTD-Mobile | DenseFusion | http://arxiv.org/abs/1901.04780v1 | ADD AUC | 69.67 |
16k > Object Detection > 3D Object Detection | DTTD-Mobile | DenseFusion | http://arxiv.org/abs/1901.04780v1 | ADD-S AUC | 85.88 |
16k > Object Detection > 3D Object Detection | DTTD-Mobile | MegaPose-RGBD | https://arxiv.org/abs/2212.06870v1 | ADD AUC | 49.02 |
16k > Object Detection > 3D Object Detection | DTTD-Mobile | MegaPose-RGBD | https://arxiv.org/abs/2212.06870v1 | ADD-S AUC | 62.44 |
16k > Object Detection > 3D Object Detection | DTTD-Mobile | BundleSDF | https://arxiv.org/abs/2303.14158v1 | ADD AUC | 46.86 |
16k > Object Detection > 3D Object Detection | DTTD-Mobile | BundleSDF | https://arxiv.org/abs/2303.14158v1 | ADD-S AUC | 55.74 |
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