220 research outputs found
The Drunkard's Odometry: Estimating Camera Motion in Deforming Scenes
Estimating camera motion in deformable scenes poses a complex and open
research challenge. Most existing non-rigid structure from motion techniques
assume to observe also static scene parts besides deforming scene parts in
order to establish an anchoring reference. However, this assumption does not
hold true in certain relevant application cases such as endoscopies. Deformable
odometry and SLAM pipelines, which tackle the most challenging scenario of
exploratory trajectories, suffer from a lack of robustness and proper
quantitative evaluation methodologies. To tackle this issue with a common
benchmark, we introduce the Drunkard's Dataset, a challenging collection of
synthetic data targeting visual navigation and reconstruction in deformable
environments. This dataset is the first large set of exploratory camera
trajectories with ground truth inside 3D scenes where every surface exhibits
non-rigid deformations over time. Simulations in realistic 3D buildings lets us
obtain a vast amount of data and ground truth labels, including camera poses,
RGB images and depth, optical flow and normal maps at high resolution and
quality. We further present a novel deformable odometry method, dubbed the
Drunkard's Odometry, which decomposes optical flow estimates into rigid-body
camera motion and non-rigid scene deformations. In order to validate our data,
our work contains an evaluation of several baselines as well as a novel
tracking error metric which does not require ground truth data. Dataset and
code: https://davidrecasens.github.io/TheDrunkard'sOdometry
Learned Semantic Multi-Sensor Depth Map Fusion
Volumetric depth map fusion based on truncated signed distance functions has
become a standard method and is used in many 3D reconstruction pipelines. In
this paper, we are generalizing this classic method in multiple ways: 1)
Semantics: Semantic information enriches the scene representation and is
incorporated into the fusion process. 2) Multi-Sensor: Depth information can
originate from different sensors or algorithms with very different noise and
outlier statistics which are considered during data fusion. 3) Scene denoising
and completion: Sensors can fail to recover depth for certain materials and
light conditions, or data is missing due to occlusions. Our method denoises the
geometry, closes holes and computes a watertight surface for every semantic
class. 4) Learning: We propose a neural network reconstruction method that
unifies all these properties within a single powerful framework. Our method
learns sensor or algorithm properties jointly with semantic depth fusion and
scene completion and can also be used as an expert system, e.g. to unify the
strengths of various photometric stereo algorithms. Our approach is the first
to unify all these properties. Experimental evaluations on both synthetic and
real data sets demonstrate clear improvements.Comment: 11 pages, 7 figures, 2 tables, accepted for the 2nd Workshop on 3D
Reconstruction in the Wild (3DRW2019) in conjunction with ICCV201
Accumulation and fractionation of trace metals in a Tunisian calcareous soil amended with farmyard manure and municipal solid waste compost
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