Sparse2Dense: Learning to Densify 3D Features for 3D Object Detection Yet, small, distant, and incomplete objects with sparse or few points are often hard to detect We present Sparse2Dense, a new framework to efficiently boost 3D detection performance by learning to densify point clouds in latent space
Sparse2Dense: Learning to Densify 3D Features for 3D Object Detection Yet, small, distant, and incomplete objects with sparse or few points are often hard to detect We present Sparse2Dense, a new framework to efficiently boost 3D detection performance by learning to densify point clouds in latent space
Sparse2Dense: Learning to Densify 3D Features for 3D Object Detection . . . Yet, small, distant, and incomplete objects with sparse or few points are often hard to detect We present Sparse2Dense, a new framework to efficiently boost 3D detection performance by learning to densify point clouds in latent space
Sparse2Dense论文笔记 - 知乎 Sparse2Dense: Learning to Densify 3D Features for 3D Object Detection 论文： code: Transfer dense point knowledge from the Dense point 3D Detector (DDet) to the Sparse point 3D Detector (SDet) 动机：small, distant, and incomplete objects with sparse or few points are often hard to detect
Sparse2Dense: Learning to Densify 3D Features for 3D Object Detection . . . So, in inference, SDet can simulate dense 3D features from regular (sparse) point cloud inputs without requiring dense inputs We evaluate our method on the large-scale Waymo Open Dataset and the Waymo Domain Adaptation Dataset, showing its high performance and efficiency over the state of the arts
Sparse2Dense: Learning to Densify 3D Features for 3D Object Detection Sparse2Dense: Learning to Densify 3D Features for 3D Object Detection: Paper and Code LiDAR-produced point clouds are the major source for most state-of-the-art 3D object detectors Yet, small, distant, and incomplete objects with sparse or few points are often hard to detect