51 research outputs found
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
DeepContext: Context-Encoding Neural Pathways for 3D Holistic Scene Understanding
While deep neural networks have led to human-level performance on computer
vision tasks, they have yet to demonstrate similar gains for holistic scene
understanding. In particular, 3D context has been shown to be an extremely
important cue for scene understanding - yet very little research has been done
on integrating context information with deep models. This paper presents an
approach to embed 3D context into the topology of a neural network trained to
perform holistic scene understanding. Given a depth image depicting a 3D scene,
our network aligns the observed scene with a predefined 3D scene template, and
then reasons about the existence and location of each object within the scene
template. In doing so, our model recognizes multiple objects in a single
forward pass of a 3D convolutional neural network, capturing both global scene
and local object information simultaneously. To create training data for this
3D network, we generate partly hallucinated depth images which are rendered by
replacing real objects with a repository of CAD models of the same object
category. Extensive experiments demonstrate the effectiveness of our algorithm
compared to the state-of-the-arts. Source code and data are available at
http://deepcontext.cs.princeton.edu.Comment: Accepted by ICCV201
LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop
While there has been remarkable progress in the performance of visual
recognition algorithms, the state-of-the-art models tend to be exceptionally
data-hungry. Large labeled training datasets, expensive and tedious to produce,
are required to optimize millions of parameters in deep network models. Lagging
behind the growth in model capacity, the available datasets are quickly
becoming outdated in terms of size and density. To circumvent this bottleneck,
we propose to amplify human effort through a partially automated labeling
scheme, leveraging deep learning with humans in the loop. Starting from a large
set of candidate images for each category, we iteratively sample a subset, ask
people to label them, classify the others with a trained model, split the set
into positives, negatives, and unlabeled based on the classification
confidence, and then iterate with the unlabeled set. To assess the
effectiveness of this cascading procedure and enable further progress in visual
recognition research, we construct a new image dataset, LSUN. It contains
around one million labeled images for each of 10 scene categories and 20 object
categories. We experiment with training popular convolutional networks and find
that they achieve substantial performance gains when trained on this dataset
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