3D fully convolutional network for vehicle detection in point cloud
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...ect Detection. We finally evaluate 2D detection performance on KITTI test set. Results are shown in Table5. Among the LIDAR-based methods, our “BV+FV” approach outperforms the recently proposed 3D FCN [16] method by 10.31% AP 2D in the hard setting. In overall, image-based methods usually perform better than LIDARbased methods in terms of 2D detection. This is due to the fact that image-based methods d...
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...ollowing the KITTI convention, IoU threshold is set to 0.7 for 2D boxes. Baslines. As this work aims at 3D object detection, we mainly compare our approach to LIDAR-based methods VeloFCN [17], 3D FCN [16], Vote3Deep [7] and Vote3D [26], as well as image-based methods 3DOP [4] and Mono3D [3]. For fair comparison, we focus on two variants of our approach, i.e., the purely LIDAR-based variant which uses ...
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...3 74.76 SubCNN [29] Mono 90.75 88.86 79.24 SDP+RPN [30,19] Mono 89.90 89.42 78.54 Vote3D [26] LIDAR 56.66 48.05 42.64 VeloFCN [17] LIDAR 70.68 53.45 46.90 Vote3Deep [7] LIDAR 76.95 68.39 63.22 3D FCN [16] LIDAR 85.54 75.83 68.30 Ours (BV+FV) LIDAR 89.80 79.76 78.61 Ours (BV+FV+RGB) LIDAR+Mono 90.37 88.90 79.81 Table 5: 2D detection performance: Average Precision (AP 2D) (in %) for car category on KITT...
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...Again, our method significantly outperforms previous works which include DoBEM [35] and MV3D [5] that use CNNs on projected LiDAR images, as well as 3D FCN [14] that uses 3D CNNs on voxelized point cloud....
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...We compare with two previous LiDAR only methods (VeloFCN [18] and MV3D (BV+FV) [6]) and show that our BV proposal based detector greatly outperforms VeloFCN on all cases and outperforms MV3D (BV+FV) on moderate and hard cases by a significant margin....
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...3D FCN [14] uses 3D CNNs on voxelized point cloud and is far from real-time....
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...Again, our method significantly outperforms previous works which include DoBEM [42] and MV3D [6] that use CNNs on projected LiDAR images, as well as 3D FCN [17] that uses 3D CNNs on voxelized point cloud....
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...While the model in the first row directly estimates box location and parameters from vanilla RGB-D image patch, the other one (second row) uses a FCN trained from the COCO dataset for 2D mask estimation (as that in Mask-RCNN [14]) and only uses features from the masked region for prediction....
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...In the method presented in [15], point cloud data are discretized into two-valued voxels, and then 3D convolution is applied....
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...The approach proposed in this paper is inspired by the basic idea of DenseBox....
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... 2D case. This make it possible to learn detection using a relatively simpler network structure. IV. EXPERIMENTS We evaluate the proposed 3D CNN on the vehicle detection task from the KITTI benchmark [7]. The task contains images aligned with point cloud and object info labeled by both 3D and 2D bounding boxes. The experiments mainly focus on detection of the Car category for simplicity. Regions with...
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