3D surface reconstruction with realistic feeling is a hot research field in computer graphics. In order to reconstruct 3D models with high precision, a 3D reconstruction method is proposed in this paper.Based on the BundleFusion reconstruction algorithm, this method uses color images and depth images of indoor scenes obtained by Intel’s realsenseD435i depth camera for rapid 3D reconstruction.An improved ORB feature detection algorithm is used for image feature extraction, and the efficiency of several classical feature detection algorithms in image matching is compared.Experiments show that this method can effectively generate 3d models with higher precision and better sense of reality.

Generalization to unseen environments remains a fundamental challenge in Vision-Language Navigation. To tackle this issue, we propose a novel framework that leverages world knowledge embedded within Multimodal Large Language Models. We introduce Collaborative Agents in Visual-Language Navigation (CA-VLN), a framework based on a dual-agent architecture. This architecture comprises a Knowledge Agent, which infuses the action prediction process with semantic context and commonsense reasoning, and a Hierarchical History Agent, which constructs a detailed episodic memory to enable long-horizon planning. The collaboration between these agents facilitates a dynamic interplay between high-level semantic understanding and grounded episodic experience. Extensive experiments on the R2R, REVERIE and SOON datasets demonstrate that our model achieves state-of-the-art performance, significantly improving generalization and navigation success in previously unobserved environments.

Vision-and-language navigation (VLN) traditionally relies on explicit reasoning chains, which, despite being interpretable, impose severe constraints on inference efficiency and scalability in long-range environments. Existing multimodal large language models (MLLMs) frequently encounter latency bottlenecks due to the generation of verbose textual narratives during decision-making. To address these limitations, we propose spatial reasoning vision-and-language navigation (SR-VLN), a novel framework that shifts the paradigm from explicit chain-of-thought (CoT) to an implicit spatial representation space. SR-VLN introduces a pyramidal hierarchical history framework integrated with perceptual compression to condense historical trajectories into multi-scale representations, effectively minimizing token overhead while preserving critical spatial semantics. Rather than generating verbose textual reasoning steps, SR-VLN employs compact, learnable spatial tokens (S-Tokens) to perform agile inference directly within the latent feature space. To establish robust causal mappings between these implicit states and navigational actions, we employ a hybrid training strategy that combines sparse reward supervision with reinforcement learning via GRPO. Extensive evaluations on the R2R, REVERIE, and SOON datasets demonstrate that SR-VLN achieves state-of-the-art overall navigation performance, while maintaining a comparable balance between accuracy and efficiency. Compared to explicit reasoning baselines, our method reduces token consumption by 68% and achieves a 4.1× speedup in inference while reaching a 76.02% success rate and a 73.80% SPL on the R2R unseen split, thereby facilitating near-real-time action prediction in long-range navigation environments.