Quantum Implicit Neural Representations for Novel View Synthesis introduces 3D Quantum Implicit Scene Representation (3D-QISR), a hybrid quantum‑classical radiance‑field model that replaces the classical NeRF backbone with parameterised quantum circuits. Two architectures are proposed: Full 3D-QISR, which uses a unified quantum state, and Dual‑Branch 3D-QISR, which separates spatial and view‑dependent embeddings to reduce complexity and improve scalability. Experiments on moderate‑resolution novel‑view synthesis benchmarks show that 3D‑QISR on simulated quantum hardware matches or outperforms classical baselines while using fewer than half the trainable parameters.
arXiv:2607. 00885v1 Announce Type: cross Abstract: Recent advances in neural rendering have established 3D Gaussian Splatting (3DGS) as a highly efficient representation for novel view synthesis, enabling fast training and real-time rendering with strong fidelity.
Reconstructing 3D shapes from a single image remains a fundamental yet challenging problem in computer vision. Traditional monocular 3D generation pipelines typically synthesize multiple views from a single input image before applying Neural Radiance Field (NeRF)-based reconstruction.
RoGe is a new end‑to‑end framework for novel view synthesis that jointly learns an implicit 3D scene representation and a video diffusion model. It eliminates the need for explicit 3D intermediates by querying the implicit scene with camera rays to produce geometric features that condition the diffusion model. Experiments on DL3DV show that RoGe surpasses reconstruction‑based, generation‑based, and hybrid baselines in image quality and temporal consistency, and ablations confirm the benefits of ray‑queried features and joint training.
arXiv:2609.18034v1 Announce Type: new Abstract: Novel view synthesis from unposed multi-view images remains challenging, as the model must jointly learn scene representations and camera parameters wi...
arXiv:2606. 02068v1 Announce Type: cross Abstract: Recently, novel view synthesis has witnessed remarkable progress, with mainstream methods such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) delivering impressive results.
Spackle is a lightweight residual learning framework designed to improve large-view single-image novel view synthesis (NVS) by mitigating capacity competition in hybrid decoupled systems that combine 3D Gaussian Splatting (3DGS) and diffusion models. It operates in three stages: predicting base 3DGS attributes, automatically identifying poorly reconstructed regions, and learning a residual 3DGS focused on those areas. During inference, Spackle merges the baseline and augmented Gaussians to produce high-fidelity novel views, achieving state‑of‑the‑art performance on large-view-deviation cases.
arXiv:2609.01516v1 Announce Type: new Abstract: While 3D Gaussian Splatting (3DGS) has revolutionized 3D reconstruction and novel-view synthesis, scenarios with limited input views often lead to poor...
arXiv:2608.23549v1 Announce Type: new Abstract: Rendering views using 3D scene representations such as Gaussian Splatting (3DGS), Neural Radiance Fields (NeRF), meshes, or even point clouds produces...
arXiv:2604. 05182v2 Announce Type: replace-cross Abstract: We introduce the Large Sparse Reconstruction Model to study how scaling transformer context windows affects feed-forward 3D reconstruction.
arXiv:2605.26616v2 Announce Type: replace Abstract: While 3D Gaussian Splatting has achieved remarkable success in photorealistic novel view synthesis, its pursuit of fast and high-fidelity 3D recons...
Rendering views using 3D scene representations such as Gaussian Splatting (3DGS), Neural Radiance Fields (NeRF), meshes, or even point clouds produces artifacts when input views are sparse or target v...
Cyc3D is a new benchmark for image‑to‑3D generation that evaluates both Cross‑View Object Consistency and Representation Quality. It introduces a closed‑loop View‑Cycle Structural Consistency protocol to measure geometric, perceptual, and semantic drift across repeated render‑regenerate cycles, and also assesses asset usability through geometric structure, fidelity, mesh discretization, and UV quality. Experiments show that closed‑source models outperform open‑source baselines yet still score below 48 on cycle stability, highlighting a gap between visual plausibility and robust 3D understanding.