352 research outputs found
Perspective-aware Convolution for Monocular 3D Object Detection
Monocular 3D object detection is a crucial and challenging task for
autonomous driving vehicle, while it uses only a single camera image to infer
3D objects in the scene. To address the difficulty of predicting depth using
only pictorial clue, we propose a novel perspective-aware convolutional layer
that captures long-range dependencies in images. By enforcing convolutional
kernels to extract features along the depth axis of every image pixel, we
incorporates perspective information into network architecture. We integrate
our perspective-aware convolutional layer into a 3D object detector and
demonstrate improved performance on the KITTI3D dataset, achieving a 23.9\%
average precision in the easy benchmark. These results underscore the
importance of modeling scene clues for accurate depth inference and highlight
the benefits of incorporating scene structure in network design. Our
perspective-aware convolutional layer has the potential to enhance object
detection accuracy by providing more precise and context-aware feature
extraction
Panoptic-Depth Color Map for Combination of Depth and Image Segmentation
Image segmentation and depth estimation are crucial tasks in computer vision,
especially in autonomous driving scenarios. Although these tasks are typically
addressed separately, we propose an innovative approach to combine them in our
novel deep learning network, Panoptic-DepthLab. By incorporating an additional
depth estimation branch into the segmentation network, it can predict the depth
of each instance segment. Evaluating on Cityscape dataset, we demonstrate the
effectiveness of our method in achieving high-quality segmentation results with
depth and visualize it with a color map. Our proposed method demonstrates a new
possibility of combining different tasks and networks to generate a more
comprehensive image recognition result to facilitate the safety of autonomous
driving vehicles
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