상세정보
학술지
Cocktail Glass Network: Fast Depth Estimation Using Channel to Space Unrolling
- 발행일
- 202108
- 출처
- IEEE Access, v.9, pp.114680-114689
- ISSN
- 2169-3536
- 출판사
- IEEE
- 협약과제
- 21HH5100, 객체추출 및 실-가상 정합 지원 모바일 AR 기술 개발, 고종국
- 초록
- Depth-estimation from a single input image can be used in applications such as robotics and autonomous driving. Recently, depth-estimation networks with UNet encoder/decoder structures have been widely used. In these decoders, operations are repeated to gradually increase the image resolution, while decreasing the channel size. If the upsampling operation at a high magnification can be processed at once, the amount of computation in the decoder can be dramatically reduced. To achieve this, we propose a new network structure, i.e., a cocktail glass network. In this network, convolution layers in the decoder are reduced, and a novel fast upsampling method is used that is known as channel-to-space unrolling, which converts thick channel data into high-resolution data. The proposed method can be easily implemented using simple reshaping operations; therefore, it is suitable for reducing the depth-estimation network. Considering the experimental results based on the NYU V2 and KITTI datasets, we demonstrate that the proposed method reduces the amount of computation in the decoder by half, while maintaining the same level of accuracy; it can be used in both lightweight and large-model-capacity networks.
- KSP 제안 키워드
- Channel data, Channel size, Depth estimation, Image resolution, Level of accuracy, Single-input, autonomous driving, high resolution data, network structure
본 저작물은 크리에이티브 커먼즈 저작자 표시 (CC BY) 조건에 따라 이용할 수 있습니다.
