  Recent works have demonstrated the importance of object completion in 3D
Perception from Lidar signal. Several methods have been proposed in which
modules were used to densify the point clouds produced by laser scanners,
leading to better recall and more accurate results. Pursuing in that direction,
we present, in this work, a counter-intuitive perspective: the widely-used
full-shape completion approach actually leads to a higher error-upper bound
especially for far away objects and small objects like pedestrians. Based on
this observation, we introduce a visible part completion method that requires
only 11.3\% of the prediction points that previous methods generate. To recover
the dense representation, we propose a mesh-deformation-based method to augment
the point set associated with visible foreground objects. Considering that our
approach focuses only on the visible part of the foreground objects to achieve
accurate 3D detection, we named our method What You See Is What You Detect
(WYSIWYD). Our proposed method is thus a detector-independent model that
consists of 2 parts: an Intra-Frustum Segmentation Transformer (IFST) and a
Mesh Depth Completion Network(MDCNet) that predicts the foreground depth from
mesh deformation. This way, our model does not require the time-consuming
full-depth completion task used by most pseudo-lidar-based methods. Our
experimental evaluation shows that our approach can provide up to 12.2\%
performance improvements over most of the public baseline models on the KITTI
and NuScenes dataset bringing the state-of-the-art to a new level. The codes
will be available at
\textcolor[RGB]{0,0,255}{\url{{https://github.com/Orbis36/WYSIWYD}}
