162 research outputs found
Segmentation-guided Domain Adaptation for Efficient Depth Completion
Complete depth information and efficient estimators have become vital
ingredients in scene understanding for automated driving tasks. A major problem
for LiDAR-based depth completion is the inefficient utilization of convolutions
due to the lack of coherent information as provided by the sparse nature of
uncorrelated LiDAR point clouds, which often leads to complex and
resource-demanding networks. The problem is reinforced by the expensive
aquisition of depth data for supervised training. In this work, we propose an
efficient depth completion model based on a vgg05-like CNN architecture and
propose a semi-supervised domain adaptation approach to transfer knowledge from
synthetic to real world data to improve data-efficiency and reduce the need for
a large database. In order to boost spatial coherence, we guide the learning
process using segmentations as additional source of information. The efficiency
and accuracy of our approach is evaluated on the KITTI dataset. Our approach
improves on previous efficient and low parameter state of the art approaches
while having a noticeably lower computational footprint
Enhancing Depth Completion with Multi-View Monitored Distillation
This paper presents a novel method for depth completion, which leverages
multi-view improved monitored distillation to generate more precise depth maps.
Our approach builds upon the state-of-the-art ensemble distillation method, in
which we introduce a stereo-based model as a teacher model to improve the
accuracy of the student model for depth completion. By minimizing the
reconstruction error for a given image during ensemble distillation, we can
avoid learning inherent error modes of completion-based teachers. To provide
self-supervised information, we also employ multi-view depth consistency and
multi-scale minimum reprojection. These techniques utilize existing structural
constraints to yield supervised signals for student model training, without
requiring costly ground truth depth information. Our extensive experimental
evaluation demonstrates that our proposed method significantly improves the
accuracy of the baseline monitored distillation method.Comment: 6 pages, 5 figures, references adde
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