CVSBench: A Comprehensive Benchmark for Cross-view Spatial Reasoning and Dreaming

• URL: http://arxiv.org/abs/2606.22476v1
• Date: Sun, 21 Jun 2026 12:35:43 GMT
• Title: CVSBench: A Comprehensive Benchmark for Cross-view Spatial Reasoning and Dreaming
• Abstract summary: We introduce CVSBench, a large-scale benchmark for evaluating cross-view spatial reasoning through satellite-street pairs.<n>This benchmark supports multiple tasks, including cross-view VQA, cross-view grounding, and viewpoint identification.<n>Our findings demonstrate that language-only reasoning yields marginal improvements, while incorporating visual spatial imagination via a 3D scene imagination pipeline substantially improves cross-view reasoning.
• Score: 13.534076118011603
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Humans can effortlessly reason about scenes across different viewpoints, yet it remains unclear whether Vision-Language Models (VLMs) possess similar cross-view spatial abilities. Satellite-street scene pairs, with their complex contexts and extreme viewpoint variations, provide an ideal testbed. Motivated by this, we introduce CVSBench, a large-scale benchmark for evaluating cross-view spatial reasoning through satellite-street pairs. This benchmark supports multiple tasks, including cross-view VQA, cross-view grounding, and viewpoint identification. CVSBench comprises 3,297 cross-view image groups with 9,468 object-level annotations and 40,679 question-answer (QA) pairs, enabling systematic and controlled evaluation of cross-view spatial reasoning. Extensive evaluations reveal that advanced VLMs struggle to maintain object-level and layout consistency under drastic viewpoint changes. To bridge this gap towards human-like spatial cognition, we investigate two categories of approaches: spatially grounded reasoning and the incorporation of cognitive map inputs. Our findings demonstrate that language-only reasoning yields marginal improvements, while incorporating visual spatial imagination via a 3D scene imagination pipeline substantially improves cross-view reasoning. These results highlight the necessity of explicit visual-spatial representations for robust spatial cognition in VLMs. Our data and code are released at https://huggingface.co/datasets/zlyzlyzly/CVSBench.

Related papers

• SpaceEra++: A Unified Framework Towards 3D Spatial Reasoning in Video [88.79625979053873]
  We propose a novel framework, SpaceEra, for pre-trained vision-language models (VLMs)<n>We extend the original framework into a comprehensive system, termed SpaceEra++, which spans data construction, model design, training optimization, and prompting inference.<n>In addition, to enhance spatial reasoning, we develop SpaceAlign, which enforces pairwise object constraints by jointly exploiting absolute coordinates and relative spatial relations.
  arXiv  Detail & Related papers  (2026-07-02T06:56:29Z)
• Zero-Shot 3D Question Answering via Hierarchical View-to-Token Transportation [9.296275675671636]
  We propose a hierarchical approach for input context collection at both the view and token levels.<n>Specifically, we combine pixel features with camera parameters and assess view importance based on both semantic content and geometric position.<n>We evaluate our framework on three widely used benchmarks, demonstrating significant improvements over existing tuning-free methods and performance comparable to training-based approaches.
  arXiv  Detail & Related papers  (2026-06-02T03:38:51Z)
• CrossView Suite: Harnessing Cross-view Spatial Intelligence of MLLMs with Dataset, Model and Benchmark [77.29150285469736]
  Spatial intelligence requires multimodal large language models (MLLMs) to move beyond single-view perception and reason.<n>We develop CrossView Suite across three coordinated components: CrossViewSet, CrossViewBench, and CrossViewer.<n>Our method equips an adaptive spatial region tokenizer to capture fine-grained object representations, and then aligns the multi-view objects explicitly.
  arXiv  Detail & Related papers  (2026-05-18T16:31:31Z)
• VIEW2SPACE: Studying Multi-View Visual Reasoning from Sparse Observations [47.94531550391802]
  Multi-view visual reasoning is essential for intelligent systems that must understand complex environments from sparse and discrete viewpoints.<n>In real-world scenarios, reasoning across views requires integrating partial observations without explicit guidance.<n>We leverage physically grounded simulation to construct diverse, high-fidelity 3D scenes with precise per-view metadata.
  arXiv  Detail & Related papers  (2026-03-17T13:36:30Z)
• Reasoning Path and Latent State Analysis for Multi-view Visual Spatial Reasoning: A Cognitive Science Perspective [17.592210658831902]
  spatial reasoning is a core aspect of human intelligence that allows perception, inference and planning in 3D environments.<n>Current vision-language models (VLMs) struggle to maintain geometric coherence and cross-view consistency for spatial reasoning in multi-view settings.<n>We present ReMindView-Bench, a cognitively grounded benchmark for evaluating how VLMs construct, align and maintain spatial mental models across complementary viewpoints.
  arXiv  Detail & Related papers  (2025-12-02T02:21:29Z)
• Enhancing Spatial Reasoning in Multimodal Large Language Models through Reasoning-based Segmentation [50.81551581148339]
  We introduce Relevant Reasoning (R$2$S), a reasoning-based segmentation framework.<n>We also introduce 3D ReasonSeg, a reasoning-based segmentation dataset.<n>Both experiments demonstrate that the R$2$S and 3D ReasonSeg effectively endow 3D point cloud perception with stronger spatial reasoning capabilities.
  arXiv  Detail & Related papers  (2025-06-29T06:58:08Z)
• ViewSpatial-Bench: Evaluating Multi-perspective Spatial Localization in Vision-Language Models [68.46716645478661]
  Vision-language models (VLMs) have demonstrated remarkable capabilities in understanding and reasoning about visual content.<n>Current VLMs excel primarily at egocentric spatial reasoning (from the camera's perspective) but fail to generalize to allocentric viewpoints.<n>We introduce ViewSpatial-Bench, the first comprehensive benchmark designed specifically for multi-viewpoint spatial localization recognition evaluation.
  arXiv  Detail & Related papers  (2025-05-27T17:59:26Z)
• Mind the Gap: Benchmarking Spatial Reasoning in Vision-Language Models [14.442394137843923]
  We present a detailed analysis that first delineates the core elements of spatial reasoning.<n>We then assesses the performance of these models in both synthetic and real-world images.
  arXiv  Detail & Related papers  (2025-03-25T14:34:06Z)
• Why Is Spatial Reasoning Hard for VLMs? An Attention Mechanism Perspective on Focus Areas [69.56484419619919]
  We study the spatial reasoning challenge from the lens of mechanistic interpretability.<n>We observe that successful spatial reasoning correlates strongly with the model's ability to align its attention with actual object locations.<n>Motivated by these findings, we propose ADAPTVIS to sharpen the attention on highly relevant regions when confident.
  arXiv  Detail & Related papers  (2025-03-03T17:57:03Z)

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.