2,579 research outputs found
Unsupervised Monocular Depth Estimation with Left-Right Consistency
Learning based methods have shown very promising results for the task of
depth estimation in single images. However, most existing approaches treat
depth prediction as a supervised regression problem and as a result, require
vast quantities of corresponding ground truth depth data for training. Just
recording quality depth data in a range of environments is a challenging
problem. In this paper, we innovate beyond existing approaches, replacing the
use of explicit depth data during training with easier-to-obtain binocular
stereo footage.
We propose a novel training objective that enables our convolutional neural
network to learn to perform single image depth estimation, despite the absence
of ground truth depth data. Exploiting epipolar geometry constraints, we
generate disparity images by training our network with an image reconstruction
loss. We show that solving for image reconstruction alone results in poor
quality depth images. To overcome this problem, we propose a novel training
loss that enforces consistency between the disparities produced relative to
both the left and right images, leading to improved performance and robustness
compared to existing approaches. Our method produces state of the art results
for monocular depth estimation on the KITTI driving dataset, even outperforming
supervised methods that have been trained with ground truth depth.Comment: CVPR 2017 ora
Exploiting Spatial-Temporal Context for Interacting Hand Reconstruction on Monocular RGB Video
Reconstructing interacting hands from monocular RGB data is a challenging
task, as it involves many interfering factors, e.g. self- and mutual occlusion
and similar textures. Previous works only leverage information from a single
RGB image without modeling their physically plausible relation, which leads to
inferior reconstruction results. In this work, we are dedicated to explicitly
exploiting spatial-temporal information to achieve better interacting hand
reconstruction. On one hand, we leverage temporal context to complement
insufficient information provided by the single frame, and design a novel
temporal framework with a temporal constraint for interacting hand motion
smoothness. On the other hand, we further propose an interpenetration detection
module to produce kinetically plausible interacting hands without physical
collisions. Extensive experiments are performed to validate the effectiveness
of our proposed framework, which achieves new state-of-the-art performance on
public benchmarks.Comment: 16 page
Dynamic Scene Reconstruction and Understanding
Traditional approaches to 3D reconstruction have achieved remarkable progress in static scene acquisition. The acquired data serves as priors or benchmarks for many vision and graphics tasks, such as object detection and robotic navigation. Thus, obtaining interpretable and editable representations from a raw monocular RGB-D video sequence is an outstanding goal in scene understanding. However, acquiring an interpretable representation becomes significantly more challenging when a scene contains dynamic activities; for example, a moving camera, rigid object movement, and non-rigid motions. These dynamic scene elements introduce a scene factorization problem, i.e., dividing a scene into elements and jointly estimating elements’ motion and geometry. Moreover, the monocular setting brings in the problems of tracking and fusing partially occluded objects as they are scanned from one viewpoint at a time.
This thesis explores several ideas for acquiring an interpretable model in dynamic environments. Firstly, we utilize synthetic assets such as floor plans and object meshes to generate dynamic data for training and evaluation. Then, we explore the idea of learning geometry priors with an instance segmentation module, which predicts the location and grouping of indoor objects. We use the learned geometry priors to infer the occluded object geometry for tracking and reconstruction. While instance segmentation modules usually have a generalization issue, i.e., struggling to handle unknown objects, we observed that the empty space information in the background geometry is more reliable for detecting moving objects. Thus, we proposed a segmentation-by-reconstruction strategy for acquiring rigidly-moving objects and backgrounds. Finally, we present a novel neural representation to learn a factorized scene representation, reconstructing every dynamic element. The proposed model supports both rigid and non-rigid motions without pre-trained templates. We demonstrate that our systems and representation improve the reconstruction quality on synthetic test sets and real-world scans
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