Recent advancements in deep learning have revolutionized 3D computer vision, enabling the extraction of intricate 3D information from 2D images and video sequences. This thesis explores the application of deep learning in three crucial challenges of 3D computer vision: Depth Estimation, Novel View Synthesis, and Simultaneous Localization and Mapping (SLAM). In the first part of the study, a self-supervised deep-learning method for depth estimation using a structured-light camera is proposed. Our method utilizes optical flow for improved edge preservation and reduced over-smoothing. In addition, we propose fusing depth maps from multiple video frames to enhance overall accuracy, particularly in occluded areas. Further, we demonstrate that these fused depth maps can be used for self-supervision to further improve the performance of a single-frame depth estimation network. Our models outperform state-of-the-art methods on both synthetic and real datasets. In the second part of the study, a generalizable photorealistic novel view synthesis method based on neural radiance fields (NeRF) is introduced. Our approach employs a geometry reasoner and a renderer to generate high-quality images from novel viewpoints. The geometry reasoner constructs cascaded cost volumes for each nearby source view, while the renderer utilizes a Transformer-based attention mechanism to integrate information from these cost volumes and render detailed images using volume rendering techniques. This architecture enables sophisticated occlusion reasoning and allows our method to render competitive results with per-scene optimized neural rendering methods while significantly reducing computational costs. Our experiments demonstrate superiority over state-of-the-art generalizable neural rendering models on various synthetic and real datasets. In the last part of the study, an efficient implicit neural representation method for dense visual SLAM is presented. The method reconstructs the scene representation while simultaneously estimating the camera position in a sequential manner from RGB-D frames with unknown poses. We incorporate recent advances in NeRF into the SLAM system, achieving both high accuracy and efficiency. The scene representation consists of multi-scale axis-aligned perpendicular feature planes and shallow decoders that decode the interpolated features into Truncated Signed Distance Field (TSDF) and RGB values. Extensive experiments on standard datasets demonstrate that our method outperforms state-of-the-art dense visual SLAM methods by more than 50% in 3D reconstruction and camera localization while running up to 10 times faster and eliminating the need for pre-training.