2,173 research outputs found
OctNetFusion: Learning Depth Fusion from Data
In this paper, we present a learning based approach to depth fusion, i.e.,
dense 3D reconstruction from multiple depth images. The most common approach to
depth fusion is based on averaging truncated signed distance functions, which
was originally proposed by Curless and Levoy in 1996. While this method is
simple and provides great results, it is not able to reconstruct (partially)
occluded surfaces and requires a large number frames to filter out sensor noise
and outliers. Motivated by the availability of large 3D model repositories and
recent advances in deep learning, we present a novel 3D CNN architecture that
learns to predict an implicit surface representation from the input depth maps.
Our learning based method significantly outperforms the traditional volumetric
fusion approach in terms of noise reduction and outlier suppression. By
learning the structure of real world 3D objects and scenes, our approach is
further able to reconstruct occluded regions and to fill in gaps in the
reconstruction. We demonstrate that our learning based approach outperforms
both vanilla TSDF fusion as well as TV-L1 fusion on the task of volumetric
fusion. Further, we demonstrate state-of-the-art 3D shape completion results.Comment: 3DV 2017, https://github.com/griegler/octnetfusio
Shape Completion using 3D-Encoder-Predictor CNNs and Shape Synthesis
We introduce a data-driven approach to complete partial 3D shapes through a
combination of volumetric deep neural networks and 3D shape synthesis. From a
partially-scanned input shape, our method first infers a low-resolution -- but
complete -- output. To this end, we introduce a 3D-Encoder-Predictor Network
(3D-EPN) which is composed of 3D convolutional layers. The network is trained
to predict and fill in missing data, and operates on an implicit surface
representation that encodes both known and unknown space. This allows us to
predict global structure in unknown areas at high accuracy. We then correlate
these intermediary results with 3D geometry from a shape database at test time.
In a final pass, we propose a patch-based 3D shape synthesis method that
imposes the 3D geometry from these retrieved shapes as constraints on the
coarsely-completed mesh. This synthesis process enables us to reconstruct
fine-scale detail and generate high-resolution output while respecting the
global mesh structure obtained by the 3D-EPN. Although our 3D-EPN outperforms
state-of-the-art completion method, the main contribution in our work lies in
the combination of a data-driven shape predictor and analytic 3D shape
synthesis. In our results, we show extensive evaluations on a newly-introduced
shape completion benchmark for both real-world and synthetic data
Structured Indoor Modeling
In this dissertation, we propose data-driven approaches to reconstruct 3D models for indoor scenes which are represented in a structured way (e.g., a wall is represented by a planar surface and two rooms are connected via the wall). The structured representation of models is more application ready than dense representations (e.g., a point cloud), but poses additional challenges for reconstruction since extracting structures requires high-level understanding about geometries. To address this challenging problem, we explore two common structural regularities of indoor scenes: 1) most indoor structures consist of planar surfaces (planarity), and 2) structural surfaces (e.g., walls and floor) can be represented by a 2D floorplan as a top-down view projection (orthogonality). With breakthroughs in data capturing techniques, we develop automated systems to tackle structured modeling problems, namely piece-wise planar reconstruction and floorplan reconstruction, by learning shape priors (i.e., planarity and orthogonality) from data. With structured representations and production-level quality, the reconstructed models have an immediate impact on many industrial applications
Multi-View Deep Learning for Consistent Semantic Mapping with RGB-D Cameras
Visual scene understanding is an important capability that enables robots to
purposefully act in their environment. In this paper, we propose a novel
approach to object-class segmentation from multiple RGB-D views using deep
learning. We train a deep neural network to predict object-class semantics that
is consistent from several view points in a semi-supervised way. At test time,
the semantics predictions of our network can be fused more consistently in
semantic keyframe maps than predictions of a network trained on individual
views. We base our network architecture on a recent single-view deep learning
approach to RGB and depth fusion for semantic object-class segmentation and
enhance it with multi-scale loss minimization. We obtain the camera trajectory
using RGB-D SLAM and warp the predictions of RGB-D images into ground-truth
annotated frames in order to enforce multi-view consistency during training. At
test time, predictions from multiple views are fused into keyframes. We propose
and analyze several methods for enforcing multi-view consistency during
training and testing. We evaluate the benefit of multi-view consistency
training and demonstrate that pooling of deep features and fusion over multiple
views outperforms single-view baselines on the NYUDv2 benchmark for semantic
segmentation. Our end-to-end trained network achieves state-of-the-art
performance on the NYUDv2 dataset in single-view segmentation as well as
multi-view semantic fusion.Comment: the 2017 IEEE/RSJ International Conference on Intelligent Robots and
Systems (IROS 2017
Floor-SP: Inverse CAD for Floorplans by Sequential Room-wise Shortest Path
This paper proposes a new approach for automated floorplan reconstruction
from RGBD scans, a major milestone in indoor mapping research. The approach,
dubbed Floor-SP, formulates a novel optimization problem, where room-wise
coordinate descent sequentially solves dynamic programming to optimize the
floorplan graph structure. The objective function consists of data terms guided
by deep neural networks, consistency terms encouraging adjacent rooms to share
corners and walls, and the model complexity term. The approach does not require
corner/edge detection with thresholds, unlike most other methods. We have
evaluated our system on production-quality RGBD scans of 527 apartments or
houses, including many units with non-Manhattan structures. Qualitative and
quantitative evaluations demonstrate a significant performance boost over the
current state-of-the-art. Please refer to our project website
http://jcchen.me/floor-sp/ for code and data.Comment: 10 pages, 9 figures, accepted to ICCV 201
Embodied Question Answering
We present a new AI task -- Embodied Question Answering (EmbodiedQA) -- where
an agent is spawned at a random location in a 3D environment and asked a
question ("What color is the car?"). In order to answer, the agent must first
intelligently navigate to explore the environment, gather information through
first-person (egocentric) vision, and then answer the question ("orange").
This challenging task requires a range of AI skills -- active perception,
language understanding, goal-driven navigation, commonsense reasoning, and
grounding of language into actions. In this work, we develop the environments,
end-to-end-trained reinforcement learning agents, and evaluation protocols for
EmbodiedQA.Comment: 20 pages, 13 figures, Webpage: https://embodiedqa.org
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