TL;DR
Results confirm the effectiveness of combining transformer-based global encoding with lightweight convolutional decoding for high-quality, real-time monocular depth estimation.
A hybrid self-supervised model incorporating a Skip Attention Mechanism to minimize semantic inconsistencies between encoder and decoder, and a mixed pooling method combining max and average pooling to address over-smoothing and contextual information loss is proposed.
Existing monocular depth estimation methods often struggle to preserve fine-grained spatial structures during feature encoding, which degrades the decoder’s ability to reconstruct accurate scene geometry. As a result, the predicted depth maps tend to exhibit structural blurring and depth discontinuities. To address thi...
CMambaDepth is proposed, a self-supervised framework that achieves efficient multi-scale feature fusion and fine-grained contextual modeling via channel-wise selective state propagation and a Hybrid Attention Module is introduced to combine large-kernel local context and Manhattan self-attention for complementary spati...
RGD-Net, a new depth estimation network that incorporates RGB geometric priors that can capture long-range spatial dependencies and reduce local noise interference, is suggested, and experimental results indicate that RGD-Net is promising for range-gated 3D imaging in autonomous perception tasks.
Depth estimation is a fundamental task in computer vision, essential for 3D scene reconstruction and spatial comprehension. While monocular depth estimation offers a cost-effective and flexible solution, its application to panoramic imagery is hindered by significant geometric distortions inherent in Equi-Rectangular P...
Depth perception of transparent and reflective objects has long been a critical challenge in robotic manipulation. Conventional depth sensors often fail to provide reliable measurements on such surfaces, limiting the performance of robots in perception and grasping tasks. To address this issue, we propose a novel depth...
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