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Wanjuan Su

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AAAI Conference 2024 Conference Paper

IINet: Implicit Intra-inter Information Fusion for Real-Time Stereo Matching

Ximeng Li
Chen Zhang
Wanjuan Su
Wenbing Tao

Recently, there has been a growing interest in 3D CNN-based stereo matching methods due to their remarkable accuracy. However, the high complexity of 3D convolution makes it challenging to strike a balance between accuracy and speed. Notably, explicit 3D volumes contain considerable redundancy. In this study, we delve into more compact 2D implicit network to eliminate redundancy and boost real-time performance. However, simply replacing explicit 3D networks with 2D implicit networks causes issues that can lead to performance degradation, including the loss of structural information, the quality decline of inter-image information, as well as the inaccurate regression caused by low-level features. To address these issues, we first integrate intra-image information to fuse with inter-image information, facilitating propagation guided by structural cues. Subsequently, we introduce the Fast Multi-scale Score Volume (FMSV) and Confidence Based Filtering (CBF) to efficiently acquire accurate multi-scale, noise-free inter-image information. Furthermore, combined with the Residual Context-aware Upsampler (RCU), our Intra-Inter Fusing network is meticulously designed to enhance information transmission on both feature-level and disparity-level, thereby enabling accurate and robust regression. Experimental results affirm the superiority of our network in terms of both speed and accuracy compared to all other fast methods.

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AAAI Conference 2023 Conference Paper

Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation

Wanjuan Su
Wenbing Tao

Over the years, learning-based multi-view stereo methods have achieved great success based on their coarse-to-fine depth estimation frameworks. However, 3D CNN-based cost volume regularization inevitably leads to over-smoothing problems at object boundaries due to its smooth properties. Moreover, discrete and sparse depth hypothesis sampling exacerbates the difficulty in recovering the depth of thin structures and object boundaries. To this end, we present an Efficient edge-Preserving multi-view stereo Network (EPNet) for practical depth estimation. To keep delicate estimation at details, a Hierarchical Edge-Preserving Residual learning (HEPR) module is proposed to progressively rectify the upsampling errors and help refine multi-scale depth estimation. After that, a Cross-view Photometric Consistency (CPC) is proposed to enhance the gradient flow for detailed structures, which further boosts the estimation accuracy. Last, we design a lightweight cascade framework and inject the above two strategies into it to achieve better efficiency and performance trade-offs. Extensive experiments show that our method achieves state-of-the-art performance with fast inference speed and low memory usage. Notably, our method tops the first place on challenging Tanks and Temples advanced dataset and ETH3D high-res benchmark among all published learning-based methods. Code will be available at https://github.com/susuwj/EPNet.

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