%0 Generic %1 petersen2019pix2vex %A Petersen, Felix %A Bermano, Amit H. %A Deussen, Oliver %A Cohen-Or, Daniel %D 2019 %K 3d image reconstruction single view %T Pix2Vex: Image-to-Geometry Reconstruction using a Smooth Differentiable Renderer %U http://arxiv.org/abs/1903.11149 %X The long-coveted task of reconstructing 3D geometry from images is still a standing problem. In this paper, we build on the power of neural networks and introduce Pix2Vex, a network trained to convert camera-captured images into 3D geometry. We present a novel differentiable renderer ($DR$) as a forward validation means during training. Our key insight is that $DR$s produce images of a particular appearance, different from typical input images. Hence, we propose adding an image-to-image translation component, converting between these rendering styles. This translation closes the training loop, while allowing to use minimal supervision only, without needing any 3D model as ground truth. Unlike state-of-the-art methods, our $DR$ is $C^ınfty$ smooth and thus does not display any discontinuities at occlusions or dis-occlusions. Through our novel training scheme, our network can train on different types of images, where previous work can typically only train on images of a similar appearance to those rendered by a $DR$.

@misc{petersen2019pix2vex, abstract = {The long-coveted task of reconstructing 3D geometry from images is still a standing problem. In this paper, we build on the power of neural networks and introduce Pix2Vex, a network trained to convert camera-captured images into 3D geometry. We present a novel differentiable renderer ($DR$) as a forward validation means during training. Our key insight is that $DR$s produce images of a particular appearance, different from typical input images. Hence, we propose adding an image-to-image translation component, converting between these rendering styles. This translation closes the training loop, while allowing to use minimal supervision only, without needing any 3D model as ground truth. Unlike state-of-the-art methods, our $DR$ is $C^\infty$ smooth and thus does not display any discontinuities at occlusions or dis-occlusions. Through our novel training scheme, our network can train on different types of images, where previous work can typically only train on images of a similar appearance to those rendered by a $DR$.}, added-at = {2020-01-28T16:53:17.000+0100}, author = {Petersen, Felix and Bermano, Amit H. and Deussen, Oliver and Cohen-Or, Daniel}, biburl = {https://www.bibsonomy.org/bibtex/278a70935f5b6991340a3d67f8a3a11c6/kluger}, description = {[1903.11149] Pix2Vex: Image-to-Geometry Reconstruction using a Smooth Differentiable Renderer}, interhash = {936447b38d2f9d83435f4883221e6f33}, intrahash = {78a70935f5b6991340a3d67f8a3a11c6}, keywords = {3d image reconstruction single view}, note = {cite arxiv:1903.11149}, timestamp = {2020-01-28T16:53:17.000+0100}, title = {Pix2Vex: Image-to-Geometry Reconstruction using a Smooth Differentiable Renderer}, url = {http://arxiv.org/abs/1903.11149}, year = 2019 }