Attention-based refinement for depth completion
AssigneeQUALCOMM Incorporated
InventorsYunxiao SHI, Hong CAI, Fatih Murat PORIKLI
Life of this patent
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Abstract
A processor-implemented method for attention-based depth completion includes receiving, by an artificial neural network (ANN), an input. The input includes an image and a sparse depth measurement. The ANN extracts multi-scale visual features of the input. The ANN applies a self-attention mechanism to the multi-scale visual features to generate a set of attended multi-scale visual features. The ANN generates a dense depth map based on the set of attended multi-scale visual features.
Claims28
A processor-implemented method performed by at least one processor, the processor-implemented method comprising:
- receiving, by an artificial neural network (ANN), an input comprising an image and a sparse depth measurement;
- extracting, by the ANN, multi-scale visual features of the input;
- applying, by the ANN, a self-attention mechanism to the multi-scale visual features to generate a set of attended multi-scale visual features;
- and
- generating, by the ANN, a dense depth map based on the set of attended multi-scale visual features.
An apparatus, comprising:
- at least one memory;
- and
- at least one processor coupled to the at least one memory, the at least one processor configured to:
- receive, by an artificial neural network (ANN), an input comprising an image and a sparse depth measurement;
- extract, by the ANN, multi-scale visual features of the input;
- apply, by the ANN, a self-attention mechanism to the multi-scale visual features to generate a set of attended multi-scale visual features;
- and
- generate, by the ANN, a dense depth map based on the set of attended multi-scale visual features.
A non-transitory computer-readable medium having program code recorded thereon, the program code executed by at least one processor and comprising:
- program code to receive, by an artificial neural network (ANN), an input comprising an image and a sparse depth measurement;
- program code to extract, by the ANN, multi-scale visual features of the input;
- program code to apply, by the ANN, a self-attention mechanism to the multi-scale visual features to generate a set of attended multi-scale visual features;
- and
- program code to generate, by the ANN, a dense depth map based on the set of attended multi-scale visual features.
An apparatus, comprising:
- means for receiving, by an artificial neural network (ANN), an input comprising an image and a sparse depth measurement;
- means for extracting, by the ANN, multi-scale visual features of the input;
- means for applying, by the ANN, a self-attention mechanism to the multi-scale visual features to generate a set of attended multi-scale visual features;
- and
- means for generating, by the ANN, a dense depth map based on the set of attended multi-scale visual features.
Dependencies are inferred from the claim text.