Abstract Our goal is to reconstruct scenes with stochastic, incoherent motion such as leaves moving in the wind, that can be particularly challenging because of small objects with similar appearance that move independently. Previous dynamic 3D Gaussian Splatting solutions either represent motion implicitly with neural networks achieving good quality but lower framerate, or explicitly with a function, often with higher training times and lower quality. To overcome these limitations, we propose an explicit method that introduces adaptive space‐time densification and smoother optimization. We introduce a new densification approach based on error moments that are used to guide primitive splitting, and we adaptively refine the number of keyframes used based on the variance of error. We observe that dynamic reconstruction from monocular video is hard for standard optimization pipelines. To counter this, we introduce a weighted Adam approach that improves results based on primitive visibility. Finally, to handle the hard case of independent motion of similar‐looking objects, we introduce an image‐driven as‐rigid‐as‐possible regularization. Our method has higher quality than previous explicit solutions, and has significantly higher framerate for rendering.

Neural fields excel at representing continuous visual signals but typically operate at a single, fixed resolution. We present a simple yet powerful method to optimize neural fields that can be prefiltered in a single forward pass. Key innovations and features include: (1) We perform convolutional filtering in the input domain by analytically scaling Fourier feature embeddings with the filter’s frequency response. (2) This closed-form modulation generalizes beyond Gaussian filtering and supports other parametric filters (Box and Lanczos) that are unseen at training time. (3) We train the neural field using single-sample Monte Carlo estimates of the filtered signal. Our method is fast during both training and inference, and imposes no additional constraints on the network architecture. We show quantitative and qualitative improvements over existing methods for neural-field filtering.

Radiance field methods such as 3D Gaussian Splatting (3DGS) allow easy reconstruction from photos, enabling free-viewpoint navigation. Nonetheless, pose estimation using Structure from Motion and 3DGS optimization can still each take between minutes and hours of computation after capture is complete. SLAM methods combined with 3DGS are fast but struggle with wide camera baselines and large scenes. We present an on-the-fly method to produce camera poses and a trained 3DGS immediately after capture. Our method can handle dense and wide-baseline captures of ordered photo sequences and large-scale scenes. To do this, we first introduce fast initial pose estimation, exploiting learned features and a GPU-friendly mini bundle adjustment. We then introduce direct sampling of Gaussian primitive positions and shapes, incrementally spawning primitives where required, significantly accelerating training. These two efficient steps allow fast and robust joint optimization of poses and Gaussian primitives. Our incremental approach handles large-scale scenes by introducing scalable radiance field construction, progressively clustering 3DGS primitives, storing them in anchors, and offloading them from the GPU. Clustered primitives are progressively merged, keeping the required scale of 3DGS at any viewpoint. We evaluate our solution on a variety of datasets and show that it can provide on-the-fly processing of all the capture scenarios and scene sizes we target. At the same time our method remains competitive - in speed, image quality, or both - with other methods that only handle specific capture styles or scene sizes.

We present a method to reconstruct indoor and outdoor static scene geometry and appearance from an omnidirectional video moving in a small circular sweep. This setting is challenging because of the small baseline and large depth ranges, making it difficult to find ray crossings. To better constrain the optimization, we estimate geometry as a signed distance field within a spherical binoctree data structure and use a complementary efficient tree traversal strategy based on a breadth-first search for sampling. Unlike regular grids or trees, the shape of this structure well-matches the camera setting, creating a better memory-quality trade-off. From an initial depth estimate, the binoc-tree is adaptively subdivided throughout the optimization, previous methods use a fixed depth that leaves the scene undersampled. In comparison with three neural optimization methods and two non-neural methods, ours shows de-creased geometry error on average, especially in a detailed scene, while significantly reducing the required number of voxels to represent such details

Novel view synthesis has seen major advances in recent years, with 3D Gaussian splatting offering an excellent level of visual quality, fast training and real-time rendering. However, the resources needed for training and rendering inevitably limit the size of the captured scenes that can be represented with good visual quality. We introduce a hierarchy of 3D Gaussians that preserves visual quality for very large scenes, while offering an efficient Level-of-Detail (LOD) solution for efficient rendering of distant content with effective level selection and smooth transitions between levels. We introduce a divide-and-conquer approach that allows us to train very large scenes in independent chunks. We consolidate the chunks into a hierarchy that can be optimized to further improve visual quality of Gaussians merged into intermediate nodes. Very large captures typically have sparse coverage of the scene, presenting many challenges to the original 3D Gaussian splatting training method; we adapt and regularize training to account for these issues. We present a complete solution, that enables real-time rendering of very large scenes and can adapt to available resources thanks to our LOD method. We show results for captured scenes with up to tens of thousands of images with a simple and affordable rig, covering trajectories of up to several kilometers and lasting up to one hour.

Dynamic radiance field reconstruction methods aim to model the time-varying structure and appearance of a dynamic scene. Existing methods, however, assume that accurate camera poses can be reliably estimated by Structure from Motion (SfM) algorithms. These methods, thus, are unreliable as SfM algorithms often fail or produce erroneous poses on challenging videos with highly dynamic objects, poorly textured surfaces, and rotating camera motion. We address this robustness issue by jointly estimating the static and dynamic radiance fields along with the camera parameters (poses and focal length). We demonstrate the robustness of our approach via extensive quantitative and qualitative experiments. Our results show favorable performance over the state-of-the-art dynamic view synthesis methods.

Indirect time-of-flight (iToF) imaging allows us to capture dense depth information at a low cost. However, iToF imaging often suffers from multipath interference (MPI) artifacts in the presence of scattering media, resulting in severe depth-accuracy degradation. For instance, iToF cameras cannot measure depth accurately through fog because ToF active illumination scatters back to the sensor before reaching the farther target surface. In this work, we propose a polarimetric iToF imaging method that can capture depth information robustly through scattering media. Our observations on the principle of indirect ToF imaging and polarization of light allow us to formulate a novel computational model of scattering-aware polarimetric phase measurements that enables us to correct MPI errors. We first devise a scattering-aware polarimetric iToF model that can estimate the phase of unpolarized backscattered light. We then combine the optical filtering of polarization and our computational modeling of unpolarized backscattered light via scattering analysis of phase and amplitude. This allows us to tackle the MPI problem by estimating the scattering energy through the participating media. We validate our method on an experimental setup using a customized off-the-shelf iToF camera. Our method outperforms baseline methods by a significant margin by means of our scattering model and polarimetric phase measurements.

We present an algorithm for reconstructing the radiance field of a large-scale scene from a single casually captured video. The task poses two core challenges. First, most existing radiance field reconstruction approaches rely on accurate pre-estimated camera poses from Structure-from-Motion algorithms, which frequently fail on in-the-wild videos. Second, using a single, global radiance field with finite representational capacity does not scale to longer trajectories in an unbounded scene. For handling unknown poses, we jointly estimate the camera poses with radiance field in a progressive manner. We show that progressive optimization significantly improves the robustness of the reconstruction. For handling large unbounded scenes, we dynamically allocate new local radiance fields trained with frames within a temporal window. This further improves robustness (e.g., performs well even under moderate pose drifts) and allows us to scale to large scenes. Our extensive evaluation on the TANKS AND TEMPLES dataset and our collected outdoor dataset, STATIC HIKES, show that our approach compares favorably with the state-of-the-art.

Omnidirectional videos capture environmental scenes effectively, but they have rarely been used for geometry reconstruction. In this work, we propose an egocentric 3D reconstruction method that can acquire scene geometry with high accuracy from a short egocentric omnidirectional video. To this end, we first estimate per-frame depth using a spherical disparity network. We then fuse per-frame depth estimates into a novel spherical binoctree data structure that is specifically designed to tolerate spherical depth estimation errors. By subdividing the spherical space into binary tree and octree nodes that represent spherical frustums adaptively, the spherical binoctree effectively enables egocentric surface geometry reconstruction for environmental scenes while simultaneously assigning high-resolution nodes for closely observed surfaces. This allows to reconstruct an entire scene from a short video captured with a small camera trajectory. Experimental results validate the effectiveness and accuracy of our approach for reconstructing the 3D geometry of environmental scenes from short egocentric omnidirectional video inputs. We further demonstrate various applications using a conventional omnidirectional camera, including novel-view synthesis, object insertion, and relighting of scenes using reconstructed 3D models with texture.

Multiview shape-from-shading (SfS) has achieved high-detail geometry, but its computation is expensive for solving a multiview registration and an ill-posed inverse rendering problem. Therefore, it has been mainly used for offline methods. Volumetric fusion enables real-time scanning using a conventional RGB-D camera, but its geometry resolution has been limited by the grid resolution of the volumetric distance field and depth registration errors. In this paper, we propose a real-time scanning method that can acquire high-detail geometry by bridging volumetric fusion and multiview SfS in two steps. First, we pro-pose the first real-time acquisition of photometric normals stored in texture space to achieve high-detail geometry. We also introduce geometry-aware texture mapping, which progressively refines geometric registration between the texture space and the volumetric distance field by means of normal texture, achieving real-time multiview SfS. We demonstrate our scanning of high-detail geometry using an RGB-D cam-era at ∼20 fps. Results verify that the geometry quality of our method is strongly competitive with that of offline multi-view SfS methods.

Single-shot monocular birefractive stereo methods have been used for estimating sparse depth from double refraction over edges. They also obtain an ordinary-ray (oray) image concurrently or subsequently through additional post-processing of depth densification and deconvolution. However, when an extraordinary-ray (e-ray) image is restored to acquire stereo images, the existing methods suffer from very severe restoration artifacts due to a low signal-to-noise ratio of input e-ray image or depth/deconvolution errors. In this work, we present a novel stereo image restoration network that can restore stereo images directly from a double-refraction image. First, we built a physically faithful birefractive stereo imaging dataset by simulating the double refraction phenomenon with existing RGB-D datasets. Second, we formulated a joint stereo restoration problem that accounts for not only geometric relation between o/e-ray images but also joint optimization of restoring both stereo images. We trained our model with our birefractive image dataset in an end-to-end manner. Our model restores high-quality stereo images directly from double refraction in real-time, enabling high-quality stereo video using a monocular camera. Our method also allows us to estimate dense depth maps from stereo images using a conventional stereo method. We evaluate the performance of our method experimentally and synthetically with the ground truth. Results validate that our stereo image restoration network outperforms the existing methods with high accuracy. We demonstrate several image-editing applications using our high-quality stereo images and dense depth maps.

A set of cameras with fisheye lenses have been used to capture a wide field of view. The traditional scan-line stereo algorithms based on epipolar geometry are directly inapplicable to this non-pinhole camera setup due to optical characteristics of fisheye lenses; hence, existing complete 360° RGB-D imaging systems have rarely achieved realtime performance yet. In this paper, we introduce an efficient sphere-sweeping stereo that can run directly on multiview fisheye images without requiring additional spherical rectification. Our main contributions are: First, we introduce an adaptive spherical matching method that accounts for each input fisheye camera’s resolving power concerning spherical distortion. Second, we propose a fast inter-scale bilateral cost volume filtering method that refines distance in noisy and textureless regions with optimal complexity of O(n). It enables real-time dense distance estimation while preserving edges. Lastly, the fisheye color and distance images are seamlessly combined into a complete 360° RGB-D image via fast inpainting of the dense distance map. We demonstrate an embedded 360° RGB-D imaging prototype composed of a mobile GPU and four fisheye cameras. Our prototype is capable of capturing complete 360° RGB-D videos with a resolution of two megapixels at 29 fps. Results demonstrate that our real-time method outperforms traditional omnidirectional stereo and learning-based omnidirectional stereo in terms of accuracy and performance.

Fiducial markers have been broadly used to identify objects or embed messages that can be detected by a camera. Primarily, existing detection methods assume that markers are printed on ideally planar surfaces. The size of a message or identification code is limited by the spatial resolution of binary patterns in a marker. Markers often fail to be recognized due to various imaging artifacts of optical/perspective distortion and motion blur. To overcome these limitations, we propose a novel deformable fiducial marker system that consists of three main parts: First, a fiducial marker generator creates a set of free-form color patterns to encode significantly large-scale information in unique visual codes. Second, a differentiable image simulator creates a training dataset of photorealistic scene images with the deformed markers, being rendered during optimization in a differentiable manner. The rendered images include realistic shading with specular reflection, optical distortion, defocus and motion blur, color alteration, imaging noise, and shape deformation of markers. Lastly, a trained marker detector seeks the regions of interest and recognizes multiple marker patterns simultaneously via inverse deformation transformation. The deformable marker creator and detector networks are jointly optimized via the differentiable photorealistic renderer in an end-to-end manner, allowing us to robustly recognize a wide range of deformable markers with high accuracy. Our deformable marker system is capable of decoding 36-bit messages successfully at ~29 fps with severe shape deformation. Results validate that our system significantly outperforms the traditional and data-driven marker methods. Our learning-based marker system opens up new interesting applications of fiducial markers, including cost-effective motion capture of the human body, active 3D scanning using our fiducial markers' array as structured light patterns, and robust augmented reality rendering of virtual objects on dynamic surfaces.

Cameras that capture color and depth information have become an essential imaging modality for applications in robotics, autonomous driving, virtual, and augmented reality. Existing RGB-D cameras rely on multiple sensors or active illumination with specialized sensors. In this work, we propose a method for monocular single-shot RGB-D imaging. Instead of learning depth from single-image depth cues, we revisit double-refraction imaging using a birefractive medium, measuring depth as the displacement of differently refracted images superimposed in a single capture. However, existing double-refraction methods are orders of magnitudes too slow to be used in real-time applications, e.g., in robotics, and provide only inaccurate depth due to correspondence ambiguity in double reflection. We resolve this ambiguity optically by leveraging the orthogonality of the two linearly polarized rays in double refraction -- introducing uneven double refraction by adding a linear polarizer to the birefractive medium. Doing so makes it possible to develop a real-time method for reconstructing sparse depth and color simultaneously in real-time. We validate the proposed method, both synthetically and experimentally, and demonstrate 3D object detection and photographic applications.