In January this year I started my master thesis, advised by Dr. Andreas Geiger from the Max Planck institute for Intelligent Systems and Prof. Bastian Leibe from RWTH Aachen University. Initially, I started working on OctNet , a data structure for efficient deep learning in 3D. For example, I implemented a C++/CUDA implementation of batch normalization for OctNets which I intend to publish on GitHub soon. For the master thesis, however, I concentrated on learning shape completion without ground truth, e.g. on KITTI .
The master thesis is entitled:
Learning Shape Completion from Bounding Boxes with CAD Shape Priors
Deep Learning in 3D using OctNets. Recent convolutional neural networks (CNNs) are said to outperform human performance [1, 2, 3, 4] and are, thus, receiving considerable attention. However, researchers found that generalizing CNNs to (sparse) 3D data is difficult — at least in high resolutions. In 3D applications, for example on the ModelNet 3D shape classification benchmark , researchers resorted to shallower 3D CNNs trained in low resolution [8, 9, 11]. Furthermore, 3D CNNs have been found to perform inferior to CNNs trained on 2D projections of the shapes . The hypothesis that this under-performance of 3D CNNs is due to the low resolution was recently approached in . In particular, Riegler et al. propose a novel hybrid grid-octree data structure to represent sparse 3D data, illustrated in Figure 1a, enabling efficient training of so-called OctNets in high resolutions.
Shape Completion using CAD Shape Priors. Given a voxelized point cloud of an object (for example cars in the LiDAR data of the KITTI dataset ) shape completion describes the task of predicting a dense labeling of the volume clearly identifying the space occupied by the object. As the object is observed only partially, CAD models can be employed as prior on the object’s shape. In , for example, CAD models from a hand-selected database are fitted to the object using the Iterative Closest Point (ICP) algorithm (e.g. see ). In , in contrast, the problem of selecting appropriate CAD models is avoided by learning a latent space of shapes, as illustrated in Figure 1b. Shape completion is then formulated as energy minimization over this latent space. In , the energy minimization is also guided by stereo and image information. Still, inference requires minimizing a (highly) non-linear energy for each object.
End-to-End Learning of Shape Completion in 3D using Bounding Boxes. In this thesis, we want to formulate shape completion as weakly supervised, end-to-end learning task using OctNets. Motivated by models such as , a latent shape prior is learned using deep auto-encoders or similar generative models [16, 17]. Given the voxelized point cloud as input, a second network — the inference network — is trained to directly predict the latent representation corresponding to the correct shape. Here, in contrast to energy minimization approaches, the optimal shape is directly predicted in a single forward pass. While learning high-resolution shape completion is made feasible by using OctNets, one major challenge is the missing ground truth shape data. Although CAD models could be fitted manually in order to provide ground truth for supervision, we intend to train the inference network in an unsupervised fashion on bounding boxes. Thus, given the bounding box as well as camera information, the inference network is ultimately trained in a weakly supervised fashion.
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