Aug 2026· Journal of innovative research and technology· 0 citations
TL;DR
A novel framework designed to achieve high-fidelity dynamic neural scene reconstruction from highly sparse viewpoints by integrating geometry-aware depth priors and robust multi-view correspondence constraints is proposed, offering a scalable solution for dynamic scene capture.
Abstract
The task of reconstructing dynamic neural scenes from sparse multi-view observations represents a significant challenge in computer vision and computer graphics. Traditional neural rendering techniques require dense view sampling to synthesize high-quality novel views, which is highly impractical for real-world dynamic environments where deploying numerous synchronized cameras is prohibitively expensive and logistically complex. When constrained to sparse views, existing dynamic reconstruction frameworks typically suffer from severe overfitting, resulting in pronounced geometric distortions, floaters, and temporal inconsistencies. This paper proposes a novel framework designed to achieve high-fidelity dynamic neural scene reconstruction from highly sparse viewpoints by integrating geometry-aware depth priors and robust multi-view correspondence constraints. By leveraging monocular depth estimation aligned with sparse structural cues, the system enforces a strict geometric foundation that prevents the neural field from degenerating in unobserved regions. Furthermore, we introduce a cross-view feature correspondence mechanism that penalizes photometric and geometric divergences across temporal and spatial domains, ensuring consistency in the deformation fields used to model scene dynamics. Comprehensive evaluations demonstrate that our methodology significantly suppresses artifacts and achieves superior novel view synthesis quality compared to baseline methods. The integration of these complementary constraints effectively bridges the information gap inherent in sparse observations, offering a scalable solution for dynamic scene capture.
This work aligns monocular estimates with valid multi-view geometric depths and verify their consistency to identify reliable geometric anchors, which support consistency-aware pruning and depth supervision, and aligned mono-only estimates and RGB-D joint optimization improves appearance fidelity and geometric consistency under sparse-view supervision.
This work proposes a visual relocalization method that departs from classical correspondence-based pipelines by directly estimating camera poses against a differentiable map representation built with 3D Gaussian Splatting (3DGS), and shows substantial gains in relocalization accuracy under challenging conditions.
M. Peribañez, Javier Civera, Rudolph Triebel et al.· arXiv.org· 0 citations
Generating complete 3D scenes from sparse, unconstrained views is a fundamental challenge in 3D vision which requires reasoning beyond observed content while remaining computationally tractable. Existing feed-forward reconstruction methods are inherently limited to content visible in the input images, while 3D generative modeling is hindered by the high computational cost of dense volumetric representations and the scarcity of large-scale 3D supervision. We introduce SPAR3S, a sparse voxel-aligned 3D latent generative model for conditional scene completion without requiring ground-truth 3D data for supervision. Our key insight is to formulate 3D scene generation in a structured, compact, voxel-aligned 3D latent space where only occupied voxels are represented. We learn this sparse latent space directly from multi-view images using photometric supervision via differentiable 3D Gaussian Splatting. Given a partial set of observed voxels encoded from sparse input views, scene completion reduces to predicting the missing latent tokens and their spatial support within the voxel grid. To this end, we train a masked autoregressive transformer that jointly models voxel occupancy and latent token values, enabling efficient and spatially consistent generation of unseen regions. We demonstrate the effectiveness of our method on synthetic indoor scenes, achieving higher novel-view quality than prior work. We further validate its generalization on RealEstate10k, highlighting its applicability to real-world data.
Thomas Lucas, Maxime Pietrantoni, Philippe Weinzaepfel et al.· 0 citations
Snapshot Compressive Imaging (SCI) offers an efficient solution for high-speed video acquisition and, under exposure-time camera--scene relative motion, multi-view scene capture by compressing temporal or spatial information into a single 2D measurement. While recent studies have explored SCI for 3D scene reconstruction, existing methods struggle with significant challenges due to information loss, limited viewpoint diversity, and the computational burden of jointly optimizing 3D representations and camera poses. In this work, we propose a novel framework that reconstructs high-quality 3D scenes from a single SCI measurement by leveraging 3D Gaussian Splatting (3DGS) and the powerful priors of large-scale vision foundation models (VFMs). Our primary reconstruction combines measurement-derived 3D VFM initialization with SCI-aware Gaussian optimization. After coarse-stage convergence, an auxiliary 2D VFM provides pseudo-view supervision at synthesized viewpoints for local appearance refinement. To further address the instability caused by ambiguous SCI supervision during 3DGS optimization, we introduce Opacity-Guided Splitting and Growth Regulation (OSGR), an SCI-specific densification strategy that augments split candidates using local opacity statistics, discourages loss-compensating opacity inflation through mean-opacity regulation, and bounds representation growth with explicit candidate-ratio and Gaussian-count constraints. Extensive experiments across multiple benchmarks demonstrate that our method achieves the strongest overall performance, combining leading reconstruction quality and robustness to viewpoint variation with competitive computational efficiency.
Yan-Ming Yang, Chen-Xi Song, Ping Wang et al.· 0 citations
The integration of novel view synthesis (NVS) and open-vocabulary segmentation (OVS) has recently yielded powerful feed-forward 3D foundation models. However, their inherent reliance on static-scene assumptions leads to severe misalignment of spatial features in unconstrained dynamic environments. To bridge this critical gap, we propose SPAR, a novel joint semantic-geometric encoding architecture that explicitly isolates transient dynamic noise prior to latent space aggregation. Furthermore, we introduce a dynamic-region-aware end-to-end training paradigm that structurally couples motion estimation with multi-view visual and semantic learning. This unified approach enables the network to inherently resolve motion conflicts and distill multi-view consistent, temporally stable scene representations from dynamic inputs. Extensive experiments on the challenging D-RE10K benchmark demonstrate that SPAR achieves state-of-the-art performance. Our end-to-end approach achieves exceptional novel view synthesis quality, yielding a PSNR of 22.15 dB and 23.33 dB given only 3 and 4 input views respectively. Despite being trained in a self-supervised manner, our model achieves an mIoU of 88.5% for motion mask prediction. Furthermore, our analysis reveals a strong inter-task synergy between photometric scene reconstruction and semantic understanding, where semantic synthesis learning consistently enhances photometric fidelity in novel view rendering. Code will be available at https://github.com/dmucby/SPAR.
This work introduces a system that combines the strong sequential constraints of SLAM with the flexibility and global optimization of offline SfM, enabling the metric reconstruction of arbitrary, long, uncalibrated videos.
Zador Pataki, Paul-Edouard Sarlin, Marc Pollefeys· arXiv.org· 0 citations
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