43 research outputs found
Scalable Dense Monocular Surface Reconstruction
This paper reports on a novel template-free monocular non-rigid surface
reconstruction approach. Existing techniques using motion and deformation cues
rely on multiple prior assumptions, are often computationally expensive and do
not perform equally well across the variety of data sets. In contrast, the
proposed Scalable Monocular Surface Reconstruction (SMSR) combines strengths of
several algorithms, i.e., it is scalable with the number of points, can handle
sparse and dense settings as well as different types of motions and
deformations. We estimate camera pose by singular value thresholding and
proximal gradient. Our formulation adopts alternating direction method of
multipliers which converges in linear time for large point track matrices. In
the proposed SMSR, trajectory space constraints are integrated by smoothing of
the measurement matrix. In the extensive experiments, SMSR is demonstrated to
consistently achieve state-of-the-art accuracy on a wide variety of data sets.Comment: International Conference on 3D Vision (3DV), Qingdao, China, October
201
Structure from Articulated Motion: Accurate and Stable Monocular 3D Reconstruction without Training Data
Recovery of articulated 3D structure from 2D observations is a challenging
computer vision problem with many applications. Current learning-based
approaches achieve state-of-the-art accuracy on public benchmarks but are
restricted to specific types of objects and motions covered by the training
datasets. Model-based approaches do not rely on training data but show lower
accuracy on these datasets. In this paper, we introduce a model-based method
called Structure from Articulated Motion (SfAM), which can recover multiple
object and motion types without training on extensive data collections. At the
same time, it performs on par with learning-based state-of-the-art approaches
on public benchmarks and outperforms previous non-rigid structure from motion
(NRSfM) methods. SfAM is built upon a general-purpose NRSfM technique while
integrating a soft spatio-temporal constraint on the bone lengths. We use
alternating optimization strategy to recover optimal geometry (i.e., bone
proportions) together with 3D joint positions by enforcing the bone lengths
consistency over a series of frames. SfAM is highly robust to noisy 2D
annotations, generalizes to arbitrary objects and does not rely on training
data, which is shown in extensive experiments on public benchmarks and real
video sequences. We believe that it brings a new perspective on the domain of
monocular 3D recovery of articulated structures, including human motion
capture.Comment: 21 pages, 8 figures, 2 table
Multiframe Scene Flow with Piecewise Rigid Motion
We introduce a novel multiframe scene flow approach that jointly optimizes
the consistency of the patch appearances and their local rigid motions from
RGB-D image sequences. In contrast to the competing methods, we take advantage
of an oversegmentation of the reference frame and robust optimization
techniques. We formulate scene flow recovery as a global non-linear least
squares problem which is iteratively solved by a damped Gauss-Newton approach.
As a result, we obtain a qualitatively new level of accuracy in RGB-D based
scene flow estimation which can potentially run in real-time. Our method can
handle challenging cases with rigid, piecewise rigid, articulated and moderate
non-rigid motion, and does not rely on prior knowledge about the types of
motions and deformations. Extensive experiments on synthetic and real data show
that our method outperforms state-of-the-art.Comment: International Conference on 3D Vision (3DV), Qingdao, China, October
201
NeuralClothSim: Neural Deformation Fields Meet the Kirchhoff-Love Thin Shell Theory
Cloth simulation is an extensively studied problem, with a plethora of
solutions available in computer graphics literature. Existing cloth simulators
produce realistic cloth deformations that obey different types of boundary
conditions. Nevertheless, their operational principle remains limited in
several ways: They operate on explicit surface representations with a fixed
spatial resolution, perform a series of discretised updates (which bounds their
temporal resolution), and require comparably large amounts of storage.
Moreover, back-propagating gradients through the existing solvers is often not
straightforward, which poses additional challenges when integrating them into
modern neural architectures. In response to the limitations mentioned above,
this paper takes a fundamentally different perspective on physically-plausible
cloth simulation and re-thinks this long-standing problem: We propose
NeuralClothSim, i.e., a new cloth simulation approach using thin shells, in
which surface evolution is encoded in neural network weights. Our
memory-efficient and differentiable solver operates on a new continuous
coordinate-based representation of dynamic surfaces, i.e., neural deformation
fields (NDFs); it supervises NDF evolution with the rules of the non-linear
Kirchhoff-Love shell theory. NDFs are adaptive in the sense that they 1)
allocate their capacity to the deformation details as the latter arise during
the cloth evolution and 2) allow surface state queries at arbitrary spatial and
temporal resolutions without retraining. We show how to train our
NeuralClothSim solver while imposing hard boundary conditions and demonstrate
multiple applications, such as material interpolation and simulation editing.
The experimental results highlight the effectiveness of our formulation and its
potential impact.Comment: 27 pages, 22 figures and 3 tables; project page:
https://4dqv.mpi-inf.mpg.de/NeuralClothSim
EventNeRF: Neural Radiance Fields from a Single Colour Event Camera
Asynchronously operating event cameras find many applications due to their
high dynamic range, no motion blur, low latency and low data bandwidth. The
field has seen remarkable progress during the last few years, and existing
event-based 3D reconstruction approaches recover sparse point clouds of the
scene. However, such sparsity is a limiting factor in many cases, especially in
computer vision and graphics, that has not been addressed satisfactorily so
far. Accordingly, this paper proposes the first approach for 3D-consistent,
dense and photorealistic novel view synthesis using just a single colour event
stream as input. At the core of our method is a neural radiance field trained
entirely in a self-supervised manner from events while preserving the original
resolution of the colour event channels. Next, our ray sampling strategy is
tailored to events and allows for data-efficient training. At test, our method
produces results in the RGB space at unprecedented quality. We evaluate our
method qualitatively and quantitatively on several challenging synthetic and
real scenes and show that it produces significantly denser and more visually
appealing renderings than the existing methods. We also demonstrate robustness
in challenging scenarios with fast motion and under low lighting conditions. We
will release our dataset and our source code to facilitate the research field,
see https://4dqv.mpi-inf.mpg.de/EventNeRF/.Comment: 18 pages, 18 figures, 3 table