Feed-Forward 3D综述：三维视觉如何「一步到位」

Core Insights - The article discusses advancements in the field of 3D vision, particularly focusing on the transition from traditional methods to Feed-Forward 3D approaches, which enhance efficiency and generalization capabilities [2][4]. Summary by Sections Overview of Feed-Forward 3D - The article highlights the evolution of 3D reconstruction techniques, from Structure-from-Motion (SfM) to Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), emphasizing the shift towards Feed-Forward 3D methods that eliminate the need for per-scene optimization [2][6]. Key Technological Branches - Five main architectural categories of Feed-Forward 3D methods are identified, each contributing significantly to the field's progress [6][7]. - Neural Radiance Fields (NeRF) introduced a differentiable framework for volume rendering but faced efficiency issues due to scene-specific optimization. The emergence of conditional NeRF has led to various branches focusing on direct prediction of radiance fields [7][9]. - PointMap Models, led by DUSt3R, predict pixel-aligned 3D point clouds directly within a Transformer framework, enhancing efficiency and memory capabilities [9][10]. - 3D Gaussian Splatting (3DGS) represents scenes as Gaussian point clouds, balancing rendering quality and speed. Recent advancements allow for direct output of Gaussian parameters [10][12]. - Mesh, Occupancy, and SDF Models integrate traditional geometric modeling with modern techniques, enabling high-precision surface modeling [14][19]. Applications and Benchmarking - The paper summarizes the application of Feed-Forward models across various tasks, including camera pose estimation, point map estimation, and single-image view synthesis, providing a comprehensive benchmark of over 30 common 3D datasets [16][18][22]. - Evaluation metrics such as PSNR, SSIM, and Chamfer Distance are established to facilitate model comparison and performance assessment [18][23]. Future Challenges and Trends - The article identifies four major open questions for future research, including the integration of Diffusion Transformers, scalable 4D memory mechanisms, and the construction of multimodal large-scale datasets [27][28]. - Challenges such as the predominance of RGB-only data, the need for improved reconstruction accuracy, and difficulties in free-viewpoint rendering are highlighted [29].