Submitted by yaqi zou.

Submission data

Full nameCost-Conditioned Multi-Receptive-Field Residual Aggregation for Stereo Matching
DescriptionCMRA-Stereo is a stereo matching framework developed for accurate and efficient depth estimation in challenging visual environments. Our research focuses on robust stereo perception, including applications in underwater robotics. We submit our results to the ETH3D two-view stereo benchmark to evaluate the method on real-world stereo images and compare its performance with existing approaches. This submission is part of our academic research.
Programming language(s)Python with CUDA (PyTorch)
HardwareNVIDIA GeForce RTX 4090 (24 GB VRAM), single GPU
Submission creation date9 Sep, 2026
Last edited9 Sep, 2026

High-res multi-view results



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indooroutdoorcourty.delive.electrofacadekickermeadowofficepipesplaygr.reliefrelief.terraceterrai.
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Low-res many-view results



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indooroutdoorlakesidesand boxstorage roomstorage room 2tunnel
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Low-res two-view results



Infoalldeliv. area 1ldeliv. area 1sdeliv. area 2ldeliv. area 2sdeliv. area 3ldeliv. area 3select. 1lelect. 1select. 2lelect. 2select. 3lelect. 3sfacade 1sforest 1sforest 2splayg. 1lplayg. 1splayg. 2lplayg. 2splayg. 3lplayg. 3sterra. 1sterra. 2sterra. 1lterra. 1sterra. 2lterra. 2s
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SLAM results



allboxesboxes darkbuddhacables 4cables 5desk 1desk 2desk changing 2desk dark 1desk dark 2desk global light changesdesk ir lightdinodroneforeground occlusionhelmetkidnap 2lamplarge loop 2large loop 3large non loopmotion 2motion 3motion 4planar 1reflective 2scale changetable 1table 2table 5table 6table global light changestable local light changestable scenetrashbin
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