We present a unified, efficient and effective framework for point-cloud based 3D object
detection. Our two-stage approach utilizes both voxel representation and raw point cloud data to …

… that fixes the disadvantages of R-CNN and SPPnet, while improving on their speed and
accuracy. We call this method Fast R-CNN because it’s comparatively fast to train and test. The …

… Mask R-CNN is simple to train and adds only a small overhead to Faster R-CNN, running
at 5 … , including pixel-to-pixel alignment, which is the main missing piece of Fast/Faster R-CNN. …

… Mask R-CNN is simple to train and adds only a small overhead to Faster R-CNN, running
at 5 … , including pixel-to-pixel alignment, which is the main missing piece of Fast/Faster R-CNN. …

… the Fast R-CNN approach. This is followed by the details of the Faster R-CNN approach. …
In this table we observe that the Fast R-CNN and Faster R-CNN have the same mAP which …

… This paper proposes an improved Faster R-CNN algorithm that can accurately … point rectangle
loss (CR Loss) is designed as the bounding box regression loss function of Faster R-CNN…

… We incorporate local keypoints in Faster R-CNN to improve object detection. The designed
keypoint anchors are coupled with Faster anchors to cover the deficiency of Faster R-CNN, …

… an improved algorithm based on faster region-based CNN (Faster R-CNN) for small object
… of Faster R-CNN, this paper proposes an improved method based on Faster R-CNN for small …

… cracks, and Faster R-CNN and Mask R-CNN are compared and … to ensure that both Faster
R-CNN and Mask R-CNN complete the … The following points in this study can be concluded. …

… We sample 128 RoIs per image in Faster R-CNN [25] based model and 512 RoIs per image
in FPN [17] based model, with the 1:3 ratio of positive to negative. RoIAlign [11] is adopted in …