Omnnidirectional Stereo Depth Estimation
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Benchmarks
Helvipad
Most implemented
Pyramid Stereo Matching Network
Boosting Omnidirectional Stereo Matching with a Pre-trained Depth Foundation Model
Helvipad: A Real-World Dataset for Omnidirectional Stereo Depth Estimation
Iterative Geometry Encoding Volume for Stereo Matching
PSMNet: Position-aware Stereo Merging Network for Room Layout Estimation
360SD-Net: 360° Stereo Depth Estimation with Learnable Cost Volume
Papers
Boosting Omnidirectional Stereo Matching with a Pre-trained Depth Foundation Model
Omnidirectional depth perception is essential for mobile robotics applications that require scene understanding across a full 360{\deg} field of view. Camera-based setups offer a cost-effective option by using stereo dep…
Depth EstimationMonocular Depth EstimationOmnnidirectional Stereo Depth EstimationScene Understanding+2Helvipad: A Real-World Dataset for Omnidirectional Stereo Depth Estimation
Despite progress in stereo depth estimation, omnidirectional imaging remains underexplored, mainly due to the lack of appropriate data. We introduce Helvipad, a real-world dataset for omnidirectional stereo depth estimat…
Depth CompletionDepth EstimationOmnnidirectional Stereo Depth EstimationStereo Depth EstimationIterative Geometry Encoding Volume for Stereo Matching
Recurrent All-Pairs Field Transforms (RAFT) has shown great potentials in matching tasks. However, all-pairs correlations lack non-local geometry knowledge and have difficulties tackling local ambiguities in ill-posed re…
Omnnidirectional Stereo Depth EstimationStereo MatchingPSMNet: Position-aware Stereo Merging Network for Room Layout Estimation
In this paper, we propose a new deep learning-based method for estimating room layout given a pair of 360 panoramas. Our system, called Position-aware Stereo Merging Network or PSMNet, is an end-to-end joint layout-pose …
Omnnidirectional Stereo Depth EstimationPositionRoom Layout Estimation360SD-Net: 360° Stereo Depth Estimation with Learnable Cost Volume
Recently, end-to-end trainable deep neural networks have significantly improved stereo depth estimation for perspective images. However, 360{\deg} images captured under equirectangular projection cannot benefit from dire…
Depth EstimationOmnnidirectional Stereo Depth EstimationStereo Depth EstimationPyramid Stereo Matching Network
Recent work has shown that depth estimation from a stereo pair of images can be formulated as a supervised learning task to be resolved with convolutional neural networks (CNNs). However, current architectures rely on pa…
Depth EstimationOmnnidirectional Stereo Depth EstimationStereo Depth EstimationStereo-LiDAR Fusion+2