SwiftDepth: An Efficient Hybrid CNN-Transformer Model for Self-Supervised Monocular Depth Estimation on Mobile Devices
Summary
SwiftDepth: An Efficient Hybrid CNN-Transformer Model for Self-Supervised Monocular Depth Estimation on Mobile Devices is a scholarly article[1].
Key Facts
SwiftDepth: An Efficient Hybrid CNN-Transformer Model for Self-Supervised Monocular Depth Estimation on Mobile Devices's instance of is recorded as scholarly article[2].
References
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APA4ort.xyz Knowledge Graph. (2026). SwiftDepth: An Efficient Hybrid CNN-Transformer Model for Self-Supervised Monocular Depth Estimation on Mobile Devices. Retrieved May 24, 2026, from https://4ort.xyz/entity/swiftdepth-an-efficient-hybrid-cnn-transformer-model-for-self-supervised-monocular-depth-estimation-on-mobile-devices
MLA“SwiftDepth: An Efficient Hybrid CNN-Transformer Model for Self-Supervised Monocular Depth Estimation on Mobile Devices.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/swiftdepth-an-efficient-hybrid-cnn-transformer-model-for-self-supervised-monocular-depth-estimation-on-mobile-devices.
BibTeX@misc{4ortxyz_swiftdepth-an-efficient-hybrid-cnn-transformer-model-for-self-supervised-monocular-depth-estimation-on-mobile-devices_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{SwiftDepth: An Efficient Hybrid CNN-Transformer Model for Self-Supervised Monocular Depth Estimation on Mobile Devices}}, year = {2026}, url = {https://4ort.xyz/entity/swiftdepth-an-efficient-hybrid-cnn-transformer-model-for-self-supervised-monocular-depth-estimation-on-mobile-devices}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): SwiftDepth: An Efficient Hybrid CNN-Transformer Model for Self-Supervised Monocular Depth Estimation on Mobile Devices — https://4ort.xyz/entity/swiftdepth-an-efficient-hybrid-cnn-transformer-model-for-self-supervised-monocular-depth-estimation-on-mobile-devices (retrieved 2026-05-24)
