10,343 research outputs found
What is Holding Back Convnets for Detection?
Convolutional neural networks have recently shown excellent results in
general object detection and many other tasks. Albeit very effective, they
involve many user-defined design choices. In this paper we want to better
understand these choices by inspecting two key aspects "what did the network
learn?", and "what can the network learn?". We exploit new annotations
(Pascal3D+), to enable a new empirical analysis of the R-CNN detector. Despite
common belief, our results indicate that existing state-of-the-art convnet
architectures are not invariant to various appearance factors. In fact, all
considered networks have similar weak points which cannot be mitigated by
simply increasing the training data (architectural changes are needed). We show
that overall performance can improve when using image renderings for data
augmentation. We report the best known results on the Pascal3D+ detection and
view-point estimation tasks
Neural View-Interpolation for Sparse Light Field Video
We suggest representing light field (LF) videos as "one-off" neural networks (NN), i.e., a learned mapping from view-plus-time coordinates to high-resolution color values, trained on sparse views. Initially, this sounds like a bad idea for three main reasons: First, a NN LF will likely have less quality than a same-sized pixel basis representation. Second, only few training data, e.g., 9 exemplars per frame are available for sparse LF videos. Third, there is no generalization across LFs, but across view and time instead. Consequently, a network needs to be trained for each LF video. Surprisingly, these problems can turn into substantial advantages: Other than the linear pixel basis, a NN has to come up with a compact, non-linear i.e., more intelligent, explanation of color, conditioned on the sparse view and time coordinates. As observed for many NN however, this representation now is interpolatable: if the image output for sparse view coordinates is plausible, it is for all intermediate, continuous coordinates as well. Our specific network architecture involves a differentiable occlusion-aware warping step, which leads to a compact set of trainable parameters and consequently fast learning and fast execution
Deep Depth Completion of a Single RGB-D Image
The goal of our work is to complete the depth channel of an RGB-D image.
Commodity-grade depth cameras often fail to sense depth for shiny, bright,
transparent, and distant surfaces. To address this problem, we train a deep
network that takes an RGB image as input and predicts dense surface normals and
occlusion boundaries. Those predictions are then combined with raw depth
observations provided by the RGB-D camera to solve for depths for all pixels,
including those missing in the original observation. This method was chosen
over others (e.g., inpainting depths directly) as the result of extensive
experiments with a new depth completion benchmark dataset, where holes are
filled in training data through the rendering of surface reconstructions
created from multiview RGB-D scans. Experiments with different network inputs,
depth representations, loss functions, optimization methods, inpainting
methods, and deep depth estimation networks show that our proposed approach
provides better depth completions than these alternatives.Comment: Accepted by CVPR2018 (Spotlight). Project webpage:
http://deepcompletion.cs.princeton.edu/ This version includes supplementary
materials which provide more implementation details, quantitative evaluation,
and qualitative results. Due to file size limit, please check project website
for high-res pape
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