Accurately recovering 3D shapes from single images that contain complex backgrounds remains a longstanding and difficult task. Unlike machines, the human visual system can effortlessly filter out background distractions and utilize extensive geometric and semantic knowledge to interpret 3D structures precisely. In contrast, current single-image 3D reconstruction methods often struggle to focus on the target object when confronted with complex backgrounds, as noise and irrelevant objects reduce reconstruction accuracy. To address this issue, we propose a cross-modal fusion strategy that integrates feature enhancement with difference-guided attention to enable high-quality 3D reconstruction from single complex images (named DGGR-Net). Specifically, we utilize retrieved 3D models from the ShapeNet dataset as structural priors and introduce a local geometry-preserving graph convolution module (LGPConv) to optimize fine-grained point cloud structures. Additionally, we design a bidirectional spatial attention (BSA) module to effectively capture spatial image features, reducing background interference during feature extraction. Furthermore, we propose a difference-guided cross-modal attention (DCA) module, which explicitly computes the differences between image and point cloud features to guide precise cross-modal feature fusion, thereby improving modality complementarity and robustness. The experimental results show that our proposed method achieves a Chamfer Distance (CD) of \(3.18 \times 10^{-2}\) and an Earth Mover’s Distance (EMD) of \(3.70 \times 10^{-2}\) on the ShapeNet dataset, and \(5.62 \times 10^{-2}\) and \(7.30 \times 10^{-2}\) respectively on the Pix3D dataset. Compared with the latest method RGB2Point, our method achieved an average improvement of approximately 13.32% and 12.55% in both CD and EMD metrics across the ShapeNet and Pix3D datasets.

Diffusion models, as representative models in the field of artificial intelligence, have made significant progress in text-to-image synthesis. However, studies of style transfer using diffusion models typically require a large amount of text to describe semantic content or specific painting attributes, and the style and layout of semantic content in synthesized images are frequently uncertain. To accomplish high-quality fixed content style transfer, this paper adopts text-free guidance and proposes a multi-source, training-free and controllable style transfer method by using single image or video as content input and single or multiple style images as style guidance. To be specific, the proposed method firstly fuses the inversion noise of a content image with that of a single or multiple style images as the initial noise of stylized image sampling process. Then, the proposed method extracts the self-attention mechanism’s query, key, and value vectors from the DDIM inversion process of content and style images and injects them into the stylized image sampling process to improve the color, texture and semantics of stylized images. By setting the hyperparameters involved in the proposed method, the style transfer effect of symmetric style proportion and asymmetric style distribution can be achieved. By comparing with state-of-the-art baselines, the proposed method demonstrates high fidelity and excellent stylized performance, and can be applied to numerous image or video style transfer tasks.

Monocular depth estimation is essential for 3D perception in applications such as autonomous driving and robotics. Self-supervised methods avoid depth labels but often rely on shallow pose networks with weak temporal modeling, leading to unstable predictions. We propose EMSP-Net, an Enhanced Multi-Scale PoseNet for self-supervised monocular depth estimation. It introduces a hierarchical feature fusion encoder, a temporal attention-context decoder, and a pose consistency loss to jointly improve feature extraction, temporal stability, and geometric constraints. On the KITTI dataset, EMSP-Net achieved an absolute relative error of 0.105 and a squared relative error of 0.708. In the Make3D cross-domain test, its strong robustness was further demonstrated.

In this paper, we present a novel photometric compensation network named CASEformer, which is built upon the Swin module. For the first time, we combine coordinate attention and channel attention mechanisms to extract rich features from input images. Employing a multi-level encoder-decoder architecture with skip connections, we establish multiscale interactions between projection surfaces and projection images, achieving precise inference and compensation. Furthermore, through an attention fusion module, which simultaneously leverages both coordinate and channel information, we enhance the global context of feature maps while preserving enhanced texture coordinate details. The experimental results demonstrate the superior compensation effectiveness of our approach compared to the current state-of-the-art methods. Additionally, we propose a method for multi-surface projection compensation, further enriching our contributions.