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Found 11 papers, 7 papers with code
BANet: Bilateral Aggregation Network for Mobile Stereo Matching
State-of-the-art stereo matching methods typically use costly 3D convolutions to aggregate a full cost volume, but their computational demands make mobile deployment challenging.
StereoGen: High-quality Stereo Image Generation from a Single Image
This pipeline utilizes arbitrary single images as left images and pseudo disparities generated by a monocular depth estimation model to synthesize high-quality corresponding right images.
MonSter: Marry Monodepth to Stereo Unleashes Power
The refined monodepth is in turn guides stereo effectively at ill-posed regions.
Leveraging Consistent Spatio-Temporal Correspondence for Robust Visual Odometry
Recent approaches to VO have significantly improved performance by using deep networks to predict optical flow between video frames.
RoMeO: Robust Metric Visual Odometry
We propose Robust Metric Visual Odometry (RoMeO), a novel method that resolves these issues leveraging priors from pre-trained depth models.
IGEV++: Iterative Multi-range Geometry Encoding Volumes for Stereo Matching
We further propose a selective geometry feature fusion module to adaptively fuse multi-range and multi-granularity geometry features in MGEV.
L-MAGIC: Language Model Assisted Generation of Images with Coherence
However, the lack of global scene layout priors leads to subpar outputs with duplicated objects (e. g., multiple beds in a bedroom) or requires time-consuming human text inputs for each view.
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
