# Cylindrical Convolutional Networks for Joint Object Detection and Viewpoint Estimation

Source: [Cylindrical Convolutional Networks for Joint Object Detection and Viewpoint Estimation](<https://www.sota2.com/research/paper/68fa67411e209131cea1544d>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:10:14.206Z

Authors: Sunghun Joung, Seungryong Kim, Hanjae Kim, Minsu Kim, Ig-Jae Kim, Junghyun Cho, Kwanghoon Sohn

Published: 2020-03-25T10:24:58.000Z

## Abstract

Existing techniques to encode spatial invariance within deep convolutional neural networks only model 2D transformation fields. This does not account for the fact that objects in a 2D space are a projection of 3D ones, and thus they have limited ability to severe object viewpoint changes. To overcome this limitation, we introduce a learnable module, cylindrical convolutional networks (CCNs), that exploit cylindrical representation of a convolutional kernel defined in the 3D space. CCNs extract a view-specific feature through a view-specific convolutional kernel to predict object category scores at each viewpoint. With the view-specific feature, we simultaneously determine objective category and viewpoints using the proposed sinusoidal soft-argmax module. Our experiments demonstrate the effectiveness of the cylindrical convolutional networks on joint object detection and viewpoint estimation.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2003.11303>)

[PDF](<https://arxiv.org/pdf/2003.11303.pdf>)