19,886 research outputs found
Hierarchical Surface Prediction for 3D Object Reconstruction
Recently, Convolutional Neural Networks have shown promising results for 3D
geometry prediction. They can make predictions from very little input data such
as a single color image. A major limitation of such approaches is that they
only predict a coarse resolution voxel grid, which does not capture the surface
of the objects well. We propose a general framework, called hierarchical
surface prediction (HSP), which facilitates prediction of high resolution voxel
grids. The main insight is that it is sufficient to predict high resolution
voxels around the predicted surfaces. The exterior and interior of the objects
can be represented with coarse resolution voxels. Our approach is not dependent
on a specific input type. We show results for geometry prediction from color
images, depth images and shape completion from partial voxel grids. Our
analysis shows that our high resolution predictions are more accurate than low
resolution predictions.Comment: 3DV 201
ScanComplete: Large-Scale Scene Completion and Semantic Segmentation for 3D Scans
We introduce ScanComplete, a novel data-driven approach for taking an
incomplete 3D scan of a scene as input and predicting a complete 3D model along
with per-voxel semantic labels. The key contribution of our method is its
ability to handle large scenes with varying spatial extent, managing the cubic
growth in data size as scene size increases. To this end, we devise a
fully-convolutional generative 3D CNN model whose filter kernels are invariant
to the overall scene size. The model can be trained on scene subvolumes but
deployed on arbitrarily large scenes at test time. In addition, we propose a
coarse-to-fine inference strategy in order to produce high-resolution output
while also leveraging large input context sizes. In an extensive series of
experiments, we carefully evaluate different model design choices, considering
both deterministic and probabilistic models for completion and semantic
inference. Our results show that we outperform other methods not only in the
size of the environments handled and processing efficiency, but also with
regard to completion quality and semantic segmentation performance by a
significant margin.Comment: Video: https://youtu.be/5s5s8iH0NF
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