Recent advances in large reconstruction and generative models have significantly improved scene reconstruction and novel view generation. However, due to compute limitations, each inference with these large models is confined to a small area, making long-range consistent scene generation challenging. To address this, we propose StarGen, a novel framework that employs a pre-trained video diffusion model in an autoregressive manner for long-range scene generation. The generation of each video clip is conditioned on the 3D warping of spatially adjacent images and the tem-porally overlapping image from previously generated clips, improving spatiotemporal consistency in long-range scene generation with precise pose control. The spatiotemporal condition is compatible with various input conditions, facilitating diverse tasks, including sparse view interpolation, perpetual view generation, and layout-conditioned city generation. Quantitative and qualitative evaluations demonstrate StarGen’s superior scalability, fidelity, and pose accuracy compared to state-of-the-art methods. Project page: https://zju3dv.github.io/StarGen.

Recently, 3D Gaussian Splatting (3DGS) has attracted widespread attention due to its high-quality rendering, and ultra-fast training and rendering speed. However, due to the unstructured and irregular nature of Gaussian point clouds, it is difficult to guarantee geometric reconstruction accuracy and multi-view consistency simply by relying on image reconstruction loss. Although many studies on surface reconstruction based on 3DGS have emerged recently, the quality of their meshes is generally unsatisfactory. To address this problem, we propose a fast planar-based Gaussian splatting reconstruction representation (PGSR) to achieve high-fidelity surface reconstruction while ensuring high-quality rendering. Specifically, we first introduce an unbiased depth rendering method, which directly renders the distance from the camera origin to the Gaussian plane and the corresponding normal map based on the Gaussian distribution of the point cloud, and divides the two to obtain the unbiased depth. We then introduce single-view geometric, multi-view photometric, and geometric regularization to preserve global geometric accuracy. We also propose a camera exposure compensation model to cope with scenes with large illumination variations. Experiments on indoor and outdoor scenes show that the proposed method achieves fast training and rendering while maintaining high-fidelity rendering and geometric reconstruction, outperforming 3DGS-based and NeRF-based methods.

Dense SLAM based on monocular cameras does indeed have immense application value in the field of AR/VR, especially when it is performed on a mobile device. In this article, we propose a novel method that integrates a light-weight depth completion network into a sparse SLAM system using a multi-basis depth representation, so that dense mapping can be performed online even on a mobile phone. Specifically, we present a specifically optimized multi-basis depth completion network, called BBC-Net, tailored to the characteristics of traditional sparse SLAM systems. BBC-Net can predict multiple balanced bases and a confidence map from a monocular image with sparse points generated by off-the-shelf keypoint-based SLAM systems. The final depth is a linear combination of predicted depth bases that can be easily optimized by tuning the corresponding weights. To seamlessly incorporate the weights into traditional SLAM optimization and ensure efficiency and robustness, we design a set of depth weight factors, which makes our network a versatile plug-in module, facilitating easy integration into various existing sparse SLAM systems and significantly enhancing global depth consistency through bundle adjustment. To verify the portability of our method, we integrate BBC-Net into two representative SLAM systems. The experimental results on various datasets show that the proposed method achieves better performance in monocular dense mapping than the state-of-the-art methods. We provide an online demo running on a mobile phone, which verifies the efficiency and mapping quality of the proposed method in real-world scenarios.

We propose a real-time visual-inertial dense SLAM system that utilizes the online data streams from back-to-back dual fisheye cameras setup, providing 360◦coverage of the environment. Firstly, we employ a sliding-window-based front-end to estimate real-time poses from the binocular fisheye images and IMU data. Then, we implement a lightweight panoramic depth completion network based on multi-basis depth representation. The network takes panoramic images (obtained by stitching dual-fisheye images with extrinsics and intrinsic parameters) and sparse depths (generated by the front-end local tracking) as input and predicts multiple depth bases along with corresponding confidence as output. The final dense depth is the linear combination of the multiple depth bases. Thanks to the multi-basis depth representation, we can continuously optimize the 360° depth with the traditional optimizer to achieve higher global consistency in depth. We conducted experiments on both simulated and real-world datasets to evaluate our method. The results demonstrate that the proposed method outperforms SoTA methods in terms of depth prediction and 3D reconstruction. In addition, we develop a demo that can run on a mobile to demonstrate the real-time capabilities of our method.

In this paper, we propose a tightly-coupled SLAM system fused with RGB, Depth, IMU and structured plane information. Traditional sparse points based SLAM systems always maintain a mass of map points to model the environment. Huge number of map points bring us a high computational complexity, making it difficult to be deployed on mobile devices. On the other hand, planes are common structures in man-made environment especially in indoor environments. We usually can use a small number of planes to represent a large scene. So the main purpose of this article is to decrease the high complexity of sparse points based SLAM. We build a lightweight back-end map which consists of a few planes and map points to achieve efficient bundle adjustment (BA) with an equal or better accuracy. We use homography constraints to eliminate the parameters of numerous plane points in the optimization and reduce the complexity of BA. We separate the parameters and measurements in homography and point-to-plane constraints and compress the measurements part to further effectively im-prove the speed of BA. We also integrate the plane information into the whole system to realize robust planar feature extraction, data association, and global consistent planar reconstruction. Finally, we perform an ablation study and compare our method with similar methods in simulation and real environment data. Our system achieves obvious advantages in accuracy and efficiency. Even if the plane parameters are involved in the optimization, we effectively simplify the back-end map by using planar structures. The global bundle adjustment is nearly 2 times faster than the sparse points based SLAM algorithm.

In this paper, we present a novel monocular visual-inertial odometry system with pre-built maps deployed on the remote server, which can robustly run in real-time on a mobile device even in high latency situations. By tightly coupling VIO with geometric priors from pre-built maps, our system can tolerate the high latency and low frequency of global localization service, which is especially suitable for practical applications when the localization service is deployed on the remote server. Firstly, sparse point clouds are obtained from the dense mesh by the ray casting method according to the localization results. The dense mesh can be reconstructed from the point clouds generated by Structure-from-Motion. We directly use the sparse point clouds in feature tracking and state update to suppress drift. In the process of feature tracking, the high local accuracy of VIO is fully utilized to effectively remove outliers and make our system robust. The experiments on EurocMav datasets and simulation datasets show that compared with state-of-the-art methods, our method can achieve better results in terms of both precision and robustness. The effectiveness of the proposed method is further demonstrated through a real-time AR demo on a mobile phone with the aid of visual localization on the remote server.