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Found 4 papers, 4 papers with code
Adaptive Fusion of Single-View and Multi-View Depth for Autonomous Driving
In this work, we propose a new robustness benchmark to evaluate the depth estimation system under various noisy pose settings.
Ranked #1 on
Monocular Depth Estimation
on DDAD
MC-Stereo: Multi-peak Lookup and Cascade Search Range for Stereo Matching
This architecture mitigates the multi-peak distribution problem in matching through the multi-peak lookup strategy, and integrates the coarse-to-fine concept into the iterative framework via the cascade search range.
Accurate and Efficient Stereo Matching via Attention Concatenation Volume
In this paper, we present a novel cost volume construction method, named attention concatenation volume (ACV), which generates attention weights from correlation clues to suppress redundant information and enhance matching-related information in the concatenation volume.
Attention Concatenation Volume for Accurate and Efficient Stereo Matching
Stereo matching is a fundamental building block for many vision and robotics applications.
Ranked #1 on
Stereo Depth Estimation
on Spring
