97 research outputs found
CD: Fine-grained 3D Mesh Reconstruction with Twice Chamfer Distance
Monocular 3D reconstruction is to reconstruct the shape of object and its
other information from a single RGB image. In 3D reconstruction, polygon mesh,
with detailed surface information and low computational cost, is the most
prevalent expression form obtained from deep learning models. However, the
state-of-the-art schemes fail to directly generate well-structured meshes, and
most of meshes have two severe problems Vertices Clustering (VC) and Illegal
Twist (IT). By diving into the mesh deformation process, we pinpoint that the
inappropriate usage of Chamfer Distance (CD) loss is the root causes of VC and
IT problems in the training of deep learning model. In this paper, we initially
demonstrate these two problems induced by CD loss with visual examples and
quantitative analyses. Then, we propose a fine-grained reconstruction method
CD by employing Chamfer distance twice to perform a plausible and adaptive
deformation. Extensive experiments on two 3D datasets and comparisons with five
latest schemes demonstrate that our CD directly generates well-structured
meshes and outperforms others by alleviating VC and IT problems.Comment: under major review in TOM
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