The Less You Depend, The More You Learn: Synthesizing Novel Views from Sparse, Unposed Images without Any 3D Knowledge

• URL: http://arxiv.org/abs/2506.09885v1
• Date: Wed, 11 Jun 2025 15:57:08 GMT
• Title: The Less You Depend, The More You Learn: Synthesizing Novel Views from Sparse, Unposed Images without Any 3D Knowledge
• Authors: Haoru Wang, Kai Ye, Yangyan Li, Wenzheng Chen, Baoquan Chen,
• Abstract summary: We consider the problem of generalizable novel view synthesis (NVS)<n>NVS aims to generate novel views from sparse or even unposed 2D images without per-scene optimization.<n>We propose a novel NVS framework that minimizes 3D inductive bias and pose dependence for both input and target views.
• Score: 28.53942289386553
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We consider the problem of generalizable novel view synthesis (NVS), which aims to generate photorealistic novel views from sparse or even unposed 2D images without per-scene optimization. This task remains fundamentally challenging, as it requires inferring 3D structure from incomplete and ambiguous 2D observations. Early approaches typically rely on strong 3D knowledge, including architectural 3D inductive biases (e.g., embedding explicit 3D representations, such as NeRF or 3DGS, into network design) and ground-truth camera poses for both input and target views. While recent efforts have sought to reduce the 3D inductive bias or the dependence on known camera poses of input views, critical questions regarding the role of 3D knowledge and the necessity of circumventing its use remain under-explored. In this work, we conduct a systematic analysis on the 3D knowledge and uncover a critical trend: the performance of methods that requires less 3D knowledge accelerates more as data scales, eventually achieving performance on par with their 3D knowledge-driven counterparts, which highlights the increasing importance of reducing dependence on 3D knowledge in the era of large-scale data. Motivated by and following this trend, we propose a novel NVS framework that minimizes 3D inductive bias and pose dependence for both input and target views. By eliminating this 3D knowledge, our method fully leverages data scaling and learns implicit 3D awareness directly from sparse 2D images, without any 3D inductive bias or pose annotation during training. Extensive experiments demonstrate that our model generates photorealistic and 3D-consistent novel views, achieving even comparable performance with methods that rely on posed inputs, thereby validating the feasibility and effectiveness of our data-centric paradigm. Project page: https://pku-vcl-geometry.github.io/Less3Depend/ .

Related papers

• Does Your 3D Encoder Really Work? When Pretrain-SFT from 2D VLMs Meets 3D VLMs [72.11701578308804]
  This paper categorizes recent 3D Vision-Language Models into 3D object-centric, 2D image-based, and 3D scene-centric approaches.<n>Despite the architectural similarity of 3D scene-centric VLMs to their 2D counterparts, they have exhibited comparatively lower performance compared with the latest 3D object-centric and 2D image-based approaches.<n>Our investigation suggests that while these models possess cross-modal alignment capabilities, they tend to over-rely on linguistic cues and overfit to frequent answer distributions.
  arXiv  Detail & Related papers  (2025-06-05T17:56:12Z)
• DINeMo: Learning Neural Mesh Models with no 3D Annotations [7.21992608540601]
  Category-level 3D/6D pose estimation is a crucial step towards comprehensive 3D scene understanding.<n>Recent works explored neural mesh models that approach a range of 2D and 3D tasks from an analysis-by-synthesis perspective.<n>We present DINeMo, a novel neural mesh model that is trained with no 3D annotations by leveraging pseudo-correspondence.
  arXiv  Detail & Related papers  (2025-03-26T04:23:53Z)
• ImOV3D: Learning Open-Vocabulary Point Clouds 3D Object Detection from Only 2D Images [19.02348585677397]
  Open-vocabulary 3D object detection (OV-3Det) aims to generalize beyond the limited number of base categories labeled during the training phase. The biggest bottleneck is the scarcity of annotated 3D data, whereas 2D image datasets are abundant and richly annotated. We propose a novel framework ImOV3D to leverage pseudo multimodal representation containing both images and point clouds (PC) to close the modality gap.
  arXiv  Detail & Related papers  (2024-10-31T15:02:05Z)
• 3D-free meets 3D priors: Novel View Synthesis from a Single Image with Pretrained Diffusion Guidance [61.06034736050515]
  We introduce a method capable of generating camera-controlled viewpoints from a single input image.<n>Our method excels in handling complex and diverse scenes without extensive training or additional 3D and multiview data.
  arXiv  Detail & Related papers  (2024-08-12T13:53:40Z)
• SE3D: A Framework For Saliency Method Evaluation In 3D Imaging [4.090991964172346]
  3D Convolutional Neural Networks (3D CNNs) are able to process LIDAR, MRI, and CT scans, with significant implications for fields such as autonomous driving and medical imaging. Despite recent advances in Explainable Artificial Intelligence, little effort has been devoted to explaining 3D CNNs. We propose SE3D: a framework for Saliency method Evaluation in 3D imaging.
  arXiv  Detail & Related papers  (2024-05-23T13:55:11Z)
• Weakly Supervised Monocular 3D Detection with a Single-View Image [58.57978772009438]
  Monocular 3D detection aims for precise 3D object localization from a single-view image. We propose SKD-WM3D, a weakly supervised monocular 3D detection framework. We show that SKD-WM3D surpasses the state-of-the-art clearly and is even on par with many fully supervised methods.
  arXiv  Detail & Related papers  (2024-02-29T13:26:47Z)
• Sherpa3D: Boosting High-Fidelity Text-to-3D Generation via Coarse 3D Prior [52.44678180286886]
  2D diffusion models find a distillation approach that achieves excellent generalization and rich details without any 3D data. We propose Sherpa3D, a new text-to-3D framework that achieves high-fidelity, generalizability, and geometric consistency simultaneously.
  arXiv  Detail & Related papers  (2023-12-11T18:59:18Z)
• PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm [111.16358607889609]
  We introduce a novel universal 3D pre-training framework designed to facilitate the acquisition of efficient 3D representation.<n>For the first time, PonderV2 achieves state-of-the-art performance on 11 indoor and outdoor benchmarks, implying its effectiveness.
  arXiv  Detail & Related papers  (2023-10-12T17:59:57Z)
• Exemplar Fine-Tuning for 3D Human Model Fitting Towards In-the-Wild 3D Human Pose Estimation [107.07047303858664]
  Large-scale human datasets with 3D ground-truth annotations are difficult to obtain in the wild. We address this problem by augmenting existing 2D datasets with high-quality 3D pose fits. The resulting annotations are sufficient to train from scratch 3D pose regressor networks that outperform the current state-of-the-art on in-the-wild benchmarks.
  arXiv  Detail & Related papers  (2020-04-07T20:21:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.