CVPR 2024 论文列表

Defocus blur is a persistent problem in microscope imaging that poses harm to pathology interpretation and medical intervention in cell microscopy and microscope surgery. To address this problem a unified framework including the multi-pyramid transformer (MPT) and extended frequency contrastive regularization (EFCR) is proposed to tackle two outstanding challenges in microscopy deblur: longer attention span and data deficiency. The MPT employs an explicit pyramid structure at each network stage that integrates the cross-scale window attention (CSWA) the intra-scale channel attention (ISCA) and the feature-enhancing feed-forward network (FEFN) to capture long-range cross-scale spatial interaction and global channel context. The EFCR addresses the data deficiency problem by exploring latent deblur signals from different frequency bands. It also enables deblur knowledge transfer to learn cross-domain information from extra data improving deblur performance for labeled and unlabeled data. Extensive experiments and downstream task validation show the framework achieves state-of-the-art performance across multiple datasets. Project page: https://github.com/PieceZhang/MPT-CataBlur.

3D building reconstruction from monocular remote sensing images is an important and challenging research problem that has received increasing attention in recent years owing to its low cost of data acquisition and availability for large-scale applications. However existing methods rely on expensive 3D-annotated samples for fully-supervised training restricting their application to large-scale cross-city scenarios. In this work we propose MLS-BRN a multi-level supervised building reconstruction network that can flexibly utilize training samples with different annotation levels to achieve better reconstruction results in an end-to-end manner. To alleviate the demand on full 3D supervision we design two new modules Pseudo Building Bbox Calculator and Roof-Offset guided Footprint Extractor as well as new tasks and training strategies for different types of samples. Experimental results on several public and new datasets demonstrate that our proposed MLS-BRN achieves competitive performance using much fewer 3D-annotated samples and significantly improves the footprint extraction and 3D reconstruction performance compared with current state-of-the-art. The code and datasets of this work will be released at https://github.com/opendatalab/MLS-BRN.git.

Human perception of the world is shaped by a multitude of viewpoints and modalities. While many existing datasets focus on scene understanding from a certain perspective (e.g. egocentric or third-person views) our dataset offers a panoptic perspective (i.e. multiple viewpoints with multiple data modalities). Specifically we encapsulate third-person panoramic and front views as well as egocentric monocular/binocular views with rich modalities including video multi-channel audio directional binaural delay location data and textual scene descriptions within each scene captured presenting comprehensive observation of the world. To the best of our knowledge this is the first database that covers multiple viewpoints with multiple data modalities to mimic how daily information is accessed in the real world. Through our benchmark analysis we presented 5 different scene understanding tasks on the proposed 360+x dataset to evaluate the impact and benefit of each data modality and perspective in panoptic scene understanding. We hope this unique dataset could broaden the scope of comprehensive scene understanding and encourage the community to approach these problems from more diverse perspectives.

Recently building on the foundation of neural radiance field various techniques have emerged to learn unsigned distance fields (UDF) to reconstruct 3D non-watertight models from multi-view images. Yet a central challenge in UDF-based volume rendering is formulating a proper way to convert unsigned distance values into volume density ensuring that the resulting weight function remains unbiased and sensitive to occlusions. Falling short on these requirements often results in incorrect topology or large reconstruction errors in resulting models. This paper addresses this challenge by presenting a novel two-stage algorithm 2S-UDF for learning a high-quality UDF from multi-view images. Initially the method applies an easily trainable density function that while slightly biased and transparent aids in coarse reconstruction. The subsequent stage then refines the geometry and appearance of the object to achieve a high-quality reconstruction by directly adjusting the weight function used in volume rendering to ensure that it is unbiased and occlusion-aware. Decoupling density and weight in two stages makes our training stable and robust distinguishing our technique from existing UDF learning approaches. Evaluations on the DeepFashion3D DTU and BlendedMVS datasets validate the robustness and effectiveness of our proposed approach. In both quantitative metrics and visual quality the results indicate our superior performance over other UDF learning techniques in reconstructing 3D non-watertight models from multi-view images. Our code is available at https://bitbucket.org/jkdeng/2sudf/.