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Humans have an innate ability to sense their surroundings, as they canextract the spatial representation from the egocentric perception and form anallocentric semantic map via spatial transformation and memory updating.However, endowing mobile agents with such a spatial sensing ability is still achallenge, due to two difficulties: (1) the previous convolutional models arelimited by the local receptive field, thus, struggling to capture holisticlong-range dependencies during observation; (2) the excessive computationalbudgets required for success, often lead to a separation of the mappingpipeline into stages, resulting the entire mapping process inefficient. Toaddress these issues, we propose an end-to-end one-stage Transformer-basedframework for Mapping, termed Trans4Map. Our egocentric-to-allocentric mappingprocess includes three steps: (1) the efficient transformer extracts thecontextual features from a batch of egocentric images; (2) the proposedBidirectional Allocentric Memory (BAM) module projects egocentric features intothe allocentric memory; (3) the map decoder parses the accumulated memory andpredicts the top-down semantic segmentation map. In contrast, Trans4Mapachieves state-of-the-art results, reducing 67.2% parameters, yet gaining a+3.25% mIoU and a +4.09% mBF1 improvements on the Matterport3D dataset. Codeat: https://github.com/jamycheung/Trans4Map.

Trans4Map: Revisiting Holistic Bird's-Eye-View Mapping from Egocentric Images to Allocentric Semantics with Vision Transformers