With the rise of Extended Reality (XR) technology, there is a growing need for real-time light field reconstruction from sparse view inputs. Existing methods can be classified into offline techniques, which can generate high-quality novel views but at the cost of long inference/training time, and online methods, which either lack generalizability or produce unsatisfactory results. However, we have observed that the intrinsic sparse manifold of Multi-plane Images (MPI) enables a significant acceleration of light field reconstruction while maintaining rendering quality. Based on this insight, we introduce RealLiFe, a novel light field optimization method, which leverages the proposed Hierarchical Sparse Gradient Descent (HSGD) to produce high-quality light fields from sparse input images in real time. Technically, the coarse MPI of a scene is first generated using a 3D CNN, and it is further optimized leveraging only the scene content aligned sparse MPI gradients in a few iterations. Extensive experiments demonstrate that our method achieves comparable visual quality while being 100x faster on average than state-of-the-art offline methods and delivers better performance (about 2 dB higher in PSNR) compared to other online approaches.

With the rapid evolution of 3D generation algorithms, the cost of producing 3D humanoid character models has plummeted, yet the field is impeded by the lack of a comprehensive dataset for automatic rigging—a pivotal step in character animation. Addressing this gap, we present HumanRig, the first large-scale dataset specifically designed for 3D humanoid character rigging, encompassing 11,434 meticulously curated T-posed meshes adhered to a uniform skeleton topology. Capitalizing on this dataset, we introduce an innovative, data-driven automatic rigging framework, which overcomes the limitations of GNNbased methods in handling complex AI-generated meshes. Our approach integrates a Prior-Guided Skeleton Estimator (PGSE) module, which uses 2D skeleton joints to provide a preliminary 3D skeleton, and a Mesh-Skeleton Mutual Attention Network (MSMAN) that fuses skeleton features with 3D mesh features extracted by a U-shaped point transformer. This enables a coarse-to-fine 3D skeleton joint regression and a robust skinning estimation, surpassing previous methods in quality and versatility. This work not only remedies the dataset deficiency in rigging research but also propels the animation industry towards more efficient and automated character rigging pipelines.

Autoregressive transformers have revolutionized generative models in language processing and shown substantial promise in image and video generation. However, these models face significant challenges when extended to 3D generation tasks due to their reliance on next-token prediction to learn token sequences, which is incompatible with the unordered nature of 3D data. Instead of imposing an artificial order on 3D data, in this paper, we introduce G3PT, a scalable, coarse-to-fine 3D native generative model with cross-scale vector quantization and cross-scale autoregressive modeling. The key is to map point-based 3D data into discrete tokens with different levels of detail, naturally establishing a sequential relationship across a variety of scales suitable for autoregressive modeling. Remarkably, our method connects tokens globally across different levels of detail without manually specified ordering. Benefiting from this approach, G3PT features a versatile 3D generation pipeline that effortlessly supports the generation of 3D shapes under diverse conditional modalities. Extensive experiments demonstrate that G3PT achieves superior generation quality and generalization ability compared to previous baselines. Most importantly, for the first time in 3D generation, scaling up G3PT reveals distinct power-law scaling behaviors.

We propose XScale-NVS for high-fidelity cross-scale novel view synthesis of real-world large-scale scenes. Ex-isting representations based on explicit surface suffer from discretization resolution or$UV$distortion, while implicit volumetric representations lack scalability for large scenes due to the dispersed weight distribution and surface ambi-guity. In light of the above challenges, we introduce hash featurized manifold, a novel hash-based featurization cou-pled with a deferred neural rendering framework. This approach fully unlocks the expressivity of the representation by explicitly concentrating the hash entries on the$2D$manifold, thus effectively representing highly detailed contents independent of the discretization resolution. We also in-troduce a novel dataset, namely$Gig{a}NVS$, to benchmark cross-scale, high-resolution novel view synthesis of real-world large-scale scenes. Our method significantly outper-forms competing baselines on various real-world scenes, yielding an average LPIPS that is$\sim$40% lower than prior state-of-the-art on the challenging$Gig{a}NVS$benchmark. Please see our project page at: xscalenvs.github.io.

Towards holistic understanding of 3D scenes, a general 3D segmentation method is needed that can segment diverse objects without restrictions on object quantity or categories, while also reflecting the inherent hierarchical structure. To achieve this, we propose OmniSeg3D, an omniversal segmentation method aims for segmenting anything in 3D all at once. The key insight is to lift multi-view inconsistent 2D segmentations into a consistent 3D feature field through a hierarchical contrastive learning framework, which is accomplished by two steps. Firstly, we design a novel hierarchical representation based on category-agnostic 2D segmentations to model the multi-level relationship among pixels. Secondly, image features rendered from the 3D feature field are clustered at different levels, which can be further drawn closer or pushed apart according to the hierarchical relationship between different levels. In tackling the challenges posed by inconsistent 2D segmentations, this framework yields a global consistent 3D feature field, which further enables hierarchical segmentation, multi-object selection, and global discretization. Extensive experiments demonstrate the effectiveness of our method on high-quality 3D segmentation and accurate hierarchical structure understanding. A graphical user interface further facilitates flexible interaction for omniversal 3D segmentation.

The rapid advances of high-performance sensation empowered gigapixel-level imaging/videography for large-scale scenes, yet the abundant details in gigapixel images were rarely valued in 3d reconstruction solutions. Bridging the gap between the sensation capacity and that of reconstruction requires to attack the large-baseline challenge imposed by the large-scale scenes, while utilizing the high-resolution details provided by the gigapixel images. This paper introduces GiganticNVS for gigapixel large-scale novel view synthesis (NVS). Existing NVS methods suffer from excessively blurred artifacts and fail on the full exploitation of image resolution, due to their inefficacy of recovering a faithful underlying geometry and the dependence on dense observations to accurately interpolate radiance. Our key insight is that, a highly-expressive implicit field with view-consistency is critical for synthesizing high-fidelity details from large-baseline observations. In light of this, we propose meta-deformed manifold, where meta refers to the locally defined surface manifold whose geometry and appearance are embedded into high-dimensional latent space. Technically, meta can be decoded as neural fields using an MLP (i.e., implicit representation). Upon this novel representation, multi-view geometric correspondence can be effectively enforced with featuremetric deformation and the reflectance field can be learned purely on the surface. Experimental results verify that the proposed method outperforms state-of-the-art methods both quantitatively and qualitatively, not only on the standard datasets containing complex real-world scenes with large baseline angles, but also on the challenging gigapixel-level ultra-large-scale benchmarks.

Understanding 4D scene context in real world has become urgently critical for deploying sophisticated AI systems. In this paper, we propose a brand new scene understanding paradigm called "Context Graph Generation (CGG)", aiming at abstracting holistic semantic information in the complicated 4D world. The CGG task capitalizes on the calibrated multiview videos of a dynamic scene, and targets at recovering semantic information (coordination, trajectories and relationships) of the presented objects in the form of spatio-temporal context graph in 4D space. We also present a benchmark 4D video dataset "RealGraph", the first dataset tailored for the proposed CGG task. The raw data of RealGraph is composed of calibrated and synchronized multiview videos. We exclusively provide manual annotations including object 2D&3D bounding boxes, category labels and semantic relationships. We also make sure the annotated ID for every single object is temporally and spatially consistent. We propose the first CGG baseline algorithm, Multiview-based Context Graph Generation Network (MCGNet), to empirically investigate the legitimacy of CGG task on RealGraph dataset. We nevertheless reveal the great challenges behind this task and encourage the community to explore beyond our solution. Our project page is at https://github.com/THU-luvision/RealGraph.

This paper proposes a method for fast scene radiance field reconstruction with strong novel view synthesis performance and convenient scene editing functionality. The key idea is to fully utilize semantic parsing and primitive extraction for constraining and accelerating the radiance field reconstruction process. To fulfill this goal, a primitive-aware hybrid rendering strategy was proposed to enjoy the best of both volumetric and primitive rendering. We further contribute a reconstruction pipeline conducts primitive parsing and radiance field learning iteratively for each input frame which successfully fuses semantic, primitive, and radiance information into a single framework. Extensive evaluations demonstrate the fast reconstruction ability, high rendering quality, and convenient editing functionality of our method.

Multi-view stereopsis (MVS) recovers 3D surfaces by finding dense photo-consistent correspondences from densely sampled images. In this paper, we tackle the challenging MVS task from sparsely sampled views (up to an order of magnitude fewer images), which is more practical and cost-efficient in applications. The major challenge comes from the significant correspondence ambiguity introduced by the severe occlusions and the highly skewed patches. On the other hand, such ambiguity can be resolved by incorporating geometric cues from the global structure. In light of this, we propose ParseMVS, boosting sparse MVS by learning the P rimitive-A waR e S urface rE presentation. In particular, on top of being aware of global structure, our novel representation further allows for the preservation of fine details including geometry, texture, and visibility. More specifically, the whole scene is parsed into multiple geometric primitives. On each of them, the geometry is defined as the displacement along the primitives' normal directions, together with the texture and visibility along each view direction. An unsupervised neural network is trained to learn these factors by progressively increasing the photo-consistency and render-consistency among all input images. Since the surface properties are changed locally in the 2D space of each primitive, ParseMVS can preserve global primitive structures while optimizing local details, handling the 'incompleteness' and the 'inaccuracy' problems. We experimentally demonstrate that ParseMVS constantly outperforms the state-of-the-art surface reconstruction method in both completeness and the overall score under varying sampling sparsity, especially under the extreme sparse-MVS settings. Beyond that, ParseMVS also shows great potential in compression, robustness, and efficiency.

Self-supervised multi-view stereopsis (MVS) attracts increasing attention for learning dense surface predictions from only a set of images without onerous ground-truth 3D training data for supervision. However, existing methods highly rely on the local photometric consistency, which fails to identify accurately dense correspondence in broad textureless and reflectance areas.In this paper, we show that geometric proximity such as surface connectedness and occlusion boundaries implicitly inferred from images could serve as reliable guidance for pixel-wise multi-view correspondences. With this insight, we present a novel elastic part representation which encodes physically-connected part segmentations with elastically-varying scales, shapes and boundaries. Meanwhile, a self-supervised MVS framework namely ElasticMVS is proposed to learn the representation and estimate per-view depth following a part-aware propagation and evaluation scheme. Specifically, the pixel-wise part representation is trained by a contrastive learning-based strategy, which increases the representation compactness in geometrically concentrated areas and contrasts otherwise. ElasticMVS iteratively optimizes a part-level consistency loss and a surface smoothness loss, based on a set of depth hypotheses propagated from the geometrically concentrated parts. Extensive evaluations convey the superiority of ElasticMVS in the reconstruction completeness and accuracy, as well as the efficiency and scalability. Particularly, for the challenging large-scale reconstruction benchmark, ElasticMVS demonstrates significant performance gain over both the supervised and self-supervised approaches.

The recent success in supervised multi-view stereopsis (MVS) relies on the onerously collected real-world 3D data. While the latest differentiable rendering techniques enable unsupervised MVS, they are restricted to discretized (e.g., point cloud) or implicit geometric representation, suffering from either low integrity for a textureless region or less geometric details for complex scenes. In this paper, we propose SurRF, an unsupervised MVS pipeline by learning Surface Radiance Field, i.e., a radiance field defined on a continuous and explicit 2D surface. Our key insight is that, in a local region, the explicit surface can be gradually deformed from a continuous initialization along view-dependent camera rays by differentiable rendering. That enables us to define the radiance field only on a 2D deformable surface rather than in a dense volume of 3D space, leading to compact representation while maintaining complete shape and realistic texture for large-scale complex scenes. We experimentally demonstrate that the proposed SurRF produces competitive results over the-state-of-the-art on various real-world challenging scenes, without any 3D supervision. Moreover, SurRF shows great potential in owning the joint advantages of mesh (scene manipulation), continuous surface (high geometric resolution), and radiance field (realistic rendering).

Multiview stereopsis (MVS) methods, which can reconstruct both the 3D geometry and texture from multiple images, have been rapidly developed and extensively investigated from the feature engineering methods to the data-driven ones. However, there is no dataset containing both the 3D geometry of large-scale scenes and high-resolution observations of small details to benchmark the algorithms. To this end, we present GigaMVS, the first gigapixel-image-based 3D reconstruction benchmark for ultra-large-scale scenes. The gigapixel images, with both wide field-of-view and high-resolution details, can clearly observe both thePalace-scale scene structure andRelievo-scale local details. The ground-truth geometry is captured by the laser scanner, which covers ultra-large-scale scenes with an average area of 8667 m$^2$and a maximum area of 32007 m$^2$. Owing to the extremely large scale, complex occlusion, and gigapixel-level images, GigaMVS exposes problems that emerge from the poor scalability and efficiency of the existing MVS algorithms. We thoroughly investigate the state-of-the-art methods in terms of geometric and textural measurements, which point to the weakness of the existing methods and promising opportunities for future works. We believe that GigaMVS can benefit the community of 3D reconstruction and support the development of novel algorithms balancing robustness, scalability and accuracy.

Multi-view stereopsis (MVS) tries to recover the 3D model from 2D images. As the observations become sparser, the significant 3D information loss makes the MVS problem more challenging. Instead of only focusing on densely sampled conditions, we investigate sparse-MVS with large baseline angles since the sparser sensation is more practical and more cost-efficient. By investigating various observation sparsities, we show that the classical depth-fusion pipeline becomes powerless for the case with a larger baseline angle that worsens the photo-consistency check. As another line of the solution, we present SurfaceNet+, a volumetric method to handle the 'incompleteness' and the 'inaccuracy' problems induced by a very sparse MVS setup. Specifically, the former problem is handled by a novel volume-wise view selection approach. It owns superiority in selecting valid views while discarding invalid occluded views by considering the geometric prior. Furthermore, the latter problem is handled via a multi-scale strategy that consequently refines the recovered geometry around the region with the repeating pattern. The experiments demonstrate the tremendous performance gap between SurfaceNet+ and state-of-the-art methods in terms of precision and recall. Under the extreme sparse-MVS settings in two datasets, where existing methods can only return very few points, SurfaceNet+ still works as well as in the dense MVS setting.

In order to enable people to avoid too many cumbersome and complex operations of the command line and repeated parameter adjustments, automates pair-end whole genome re-sequence (aWGRS) data processing whereby pre-installed dependencies are presented in this paper, which are used to map reads to a reference and realign variations. This method presents aWGRS which is a method that takes as input paired-end reads and a reference genome and returns re-sequencing information. The concept behind the development of this tool is that re-sequencing requires several steps: alignment to the reference, single nucleotide polymorphisms (SNPs) calling, Insertion / Deletion (InDels) calling, structure variant (SVs) calling, and annotation. By introducing and adjusting a new concept called the recall rate, the coverage rate and accuracy rate can be met at the same time. Within the range of recall rate, a variation is evaluated by two criteria: the quality value and the number of reads that support it, and one read with higher quality value and larger supported number will be picked out finally. Genome-wide genetic variations between precocious trifoliate orange and its wild type are identified in [1], and empirical results show that there is a big reduction in the amount of variation and great improvement of accuracy between the results of aWGRS and [1] which offered by the Beijing Genomics Institute (BGI). Overall, the adjustable parameters adopted in aWGRS can affect the results of the experiment and the default filtering strategy using the mutation recall rate also can attain good results automatically.