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.
Transparent Object Depth Completion
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abstract
The perception of transparent objects for grasp and manipulation remains a major challenge, because existing robotic grasp methods which heavily rely on depth maps are not suitable for transparent objects due to their unique visual properties. These properties lead to gaps and inaccuracies in the depth maps of the transparent objects captured by depth sensors. To address this issue, we propose an end-to-end network for transparent object depth completion that combines the strengths of single-view RGB-D based depth completion and multi-view depth estimation. Moreover, we introduce a depth refinement module based on confidence estimation to fuse predicted depth maps from single-view and multi-view modules, which further refines the restored depth map. The extensive experiments on the ClearPose and TransCG datasets demonstrate that our method achieves superior accuracy and robustness in complex scenarios with significant occlusion compared to the state-of-the-art methods.
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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.