HTMNet combines a CNN-Transformer encoder, a Transformer-Mamba bottleneck fusion block, and a multi-scale attention decoder to improve depth completion for transparent and reflective objects, claiming state-of-the-art results on TransCG, ClearGrasp, and STD.
Domain randomization-enhanced depth simulation and restoration for perceiving and grasping specular and trans- parent objects,
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HTMNet: A Hybrid Network with Transformer-Mamba Bottleneck Multimodal Fusion for Transparent and Reflective Objects Depth Completion
HTMNet combines a CNN-Transformer encoder, a Transformer-Mamba bottleneck fusion block, and a multi-scale attention decoder to improve depth completion for transparent and reflective objects, claiming state-of-the-art results on TransCG, ClearGrasp, and STD.