5,239 research outputs found
Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery
One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-opera- tive morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilites by observ- ing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted in- struments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D opti- cal imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions
Guided Stereo Matching
Stereo is a prominent technique to infer dense depth maps from images, and
deep learning further pushed forward the state-of-the-art, making end-to-end
architectures unrivaled when enough data is available for training. However,
deep networks suffer from significant drops in accuracy when dealing with new
environments. Therefore, in this paper, we introduce Guided Stereo Matching, a
novel paradigm leveraging a small amount of sparse, yet reliable depth
measurements retrieved from an external source enabling to ameliorate this
weakness. The additional sparse cues required by our method can be obtained
with any strategy (e.g., a LiDAR) and used to enhance features linked to
corresponding disparity hypotheses. Our formulation is general and fully
differentiable, thus enabling to exploit the additional sparse inputs in
pre-trained deep stereo networks as well as for training a new instance from
scratch. Extensive experiments on three standard datasets and two
state-of-the-art deep architectures show that even with a small set of sparse
input cues, i) the proposed paradigm enables significant improvements to
pre-trained networks. Moreover, ii) training from scratch notably increases
accuracy and robustness to domain shifts. Finally, iii) it is suited and
effective even with traditional stereo algorithms such as SGM.Comment: CVPR 201
Sparsity Invariant CNNs
In this paper, we consider convolutional neural networks operating on sparse
inputs with an application to depth upsampling from sparse laser scan data.
First, we show that traditional convolutional networks perform poorly when
applied to sparse data even when the location of missing data is provided to
the network. To overcome this problem, we propose a simple yet effective sparse
convolution layer which explicitly considers the location of missing data
during the convolution operation. We demonstrate the benefits of the proposed
network architecture in synthetic and real experiments with respect to various
baseline approaches. Compared to dense baselines, the proposed sparse
convolution network generalizes well to novel datasets and is invariant to the
level of sparsity in the data. For our evaluation, we derive a novel dataset
from the KITTI benchmark, comprising 93k depth annotated RGB images. Our
dataset allows for training and evaluating depth upsampling and depth
prediction techniques in challenging real-world settings and will be made
available upon publication
- …