OpenNovelty.org

Vid-LLM: A Compact Video-based 3D Multimodal LLM with Reconstruction–Reasoning Synergy

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.7Download Report PDF
video-based 3D MLLMgeometric priorsCross-Task AdapterMetric Depth calibration
Abstract:

Recent developments in Multimodal Large Language Models (MLLMs) have significantly improved Vision–Language (VL) reasoning in 2D domains. However, extending these capabilities to 3D scene understanding remains a major challenge. Existing 3D Multimodal Large Language Models (3D-MLLMs) often depend on 3D data inputs, which limits scalability and generalization. To address this limitation, we propose Vid-LLM, a video-based 3D-MLLM that directly processes video inputs without requiring external 3D data, making it practical for real-world deployment. In our method, the geometric prior are directly used to improve the performance of the sceen perception. To integrate the geometric cues into the MLLM compactly, we design a Cross-Task Adapter (CTA) module to align the 3D geometric priors with the vision-language representations. To ensure geometric consistency and integrity, we introduce a Metric Depth Model that recovers real-scale geometry from the reconstruction outputs. Finally, the model is fine-tuned with a two-stage distillation optimization strategy, realizing fast convergence and stabilizes training. Extensive experiments across diverse benchmarks verified the effectiveness of our method on 3D Question Answering, 3D Dense Captioning and 3D Visual Grounding tasks, demonstrating the superior multi-task capabilities.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper proposes Vid-LLM, a video-based 3D multimodal large language model that processes video inputs without requiring external 3D data, integrating geometric priors through a Cross-Task Adapter module and metric depth recovery. Within the taxonomy, it resides in the 'Geometric Prior Integration for Video-Based 3D Reasoning' leaf under 'Video-to-3D Representation and Reconstruction'. This leaf contains only three papers total, including the original work, indicating a relatively sparse and emerging research direction focused specifically on injecting geometric cues into MLLMs for video-based 3D understanding.

The taxonomy reveals that Vid-LLM's approach sits at the intersection of multiple research threads. Its parent branch 'Video-to-3D Representation and Reconstruction' contains sibling leaves addressing multi-view spatial reasoning and 3D scene reconstruction from video, while neighboring branches explore 3D data integration with point clouds and holistic scene reasoning. The scope note explicitly distinguishes this leaf from methods requiring external 3D sensors or pre-built point clouds, positioning Vid-LLM's video-only approach as addressing a distinct gap. Related work in spatial reasoning branches focuses on viewpoint learning and metric estimation, but without the same emphasis on geometric prior integration during video processing.

Among thirty candidates examined across three contributions, none were identified as clearly refuting the proposed methods. The Vid-LLM framework, Cross-Task Adapter module, and two-stage distillation strategy each had ten candidates examined with zero refutable overlaps. This suggests that within the limited search scope, the specific combination of video-based processing, geometric prior integration via CTA, and the distillation optimization approach appears relatively distinct. However, the small candidate pool and sparse taxonomy leaf indicate this assessment reflects top-thirty semantic matches rather than exhaustive field coverage, particularly given the nascent state of geometric prior integration research.

The analysis indicates the work occupies a sparsely populated research direction within a broader field that has multiple active branches. The limited literature search scope and absence of refuting candidates among thirty examined papers suggest potential novelty, though the small taxonomy leaf size also reflects that this specific integration approach is still emerging. A more comprehensive search beyond top-thirty semantic matches would be needed to fully assess whether similar geometric prior integration strategies exist in adjacent research areas or recent preprints.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
30
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: 3D scene understanding from video using multimodal large language models. The field has evolved into several distinct branches that reflect different strategies for bridging video, language, and 3D spatial reasoning. Video-to-3D Representation and Reconstruction focuses on extracting geometric structure from temporal sequences, often integrating depth estimation or multi-view consistency to build coherent 3D models. 3D Data Integration with Multimodal LLMs emphasizes how to encode point clouds, meshes, or voxel grids alongside language and vision modalities, enabling models like Scene-llm[17] and Gpt4scene[1] to reason about pre-existing 3D data. Spatial Reasoning and Visual-Spatial Intelligence targets the core challenge of understanding object relations, affordances, and layout from visual input, with works such as Thinking in Space[2] and Mm-spatial[24] exploring how MLLMs can develop richer spatial awareness. General Video Understanding with MLLMs addresses temporal dynamics and event comprehension in videos more broadly, while Domain-Specific Applications and Language-Driven 3D Scene Synthesis branches tackle specialized settings like autonomous driving, robotics, and text-to-3D generation. Surveys and Meta-Analyses provide overarching perspectives on how these threads interconnect. A particularly active line of work explores how to inject geometric priors—such as depth maps, camera poses, or multi-view cues—into video-based reasoning pipelines, enabling models to move beyond purely appearance-based features. Vid-LLM[0] sits within this geometric prior integration cluster, emphasizing the use of structured 3D information to guide multimodal understanding of dynamic scenes. This approach contrasts with purely data-driven video models like Videollama[9] or Oryx MLLM[28], which rely heavily on large-scale pretraining without explicit geometric scaffolding. Nearby works such as Video-3D LLM[23] and Learning from Videos 3D[19] similarly leverage geometric cues but differ in how they fuse temporal and spatial representations. A central open question across these branches is how to balance the richness of learned video features with the precision of explicit 3D structure, and whether hybrid architectures can achieve both generalization and fine-grained spatial accuracy in diverse real-world scenarios.

Claimed Contributions

Vid-LLM: A video-based 3D-MLLM framework

The authors introduce Vid-LLM, a compact framework that performs 3D scene understanding and vision-language reasoning directly from monocular video inputs, eliminating the need for explicit 3D data such as point clouds or depth maps. This design improves scalability and practical deployment compared to existing 3D-MLLMs.

10 retrieved papers
Cross-Task Adapter (CTA) module

The authors design a Cross-Task Adapter that uses learnable bridge tokens to bidirectionally fuse geometric and semantic feature streams. This module enables intrinsic geometry-semantics interaction, allowing reconstruction and reasoning tasks to mutually reinforce each other within the MLLM.

10 retrieved papers
Two-stage distillation optimization strategy

The authors introduce a two-stage training strategy where Stage 1 performs dual-teacher distillation to transfer geometric and semantic knowledge, and Stage 2 jointly optimizes reconstruction and 3D vision-language tasks. This approach ensures faster convergence and improved training stability.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Vid-LLM: A video-based 3D-MLLM framework

The authors introduce Vid-LLM, a compact framework that performs 3D scene understanding and vision-language reasoning directly from monocular video inputs, eliminating the need for explicit 3D data such as point clouds or depth maps. This design improves scalability and practical deployment compared to existing 3D-MLLMs.

Contribution

Cross-Task Adapter (CTA) module

The authors design a Cross-Task Adapter that uses learnable bridge tokens to bidirectionally fuse geometric and semantic feature streams. This module enables intrinsic geometry-semantics interaction, allowing reconstruction and reasoning tasks to mutually reinforce each other within the MLLM.

Contribution

Two-stage distillation optimization strategy

The authors introduce a two-stage training strategy where Stage 1 performs dual-teacher distillation to transfer geometric and semantic knowledge, and Stage 2 jointly optimizes reconstruction and 3D vision-language tasks. This approach ensures faster convergence and improved training stability.

Table of Contents

Vid-LLM: A Compact Video-based 3D Multimodal LLM with Reconstruction–Reasoning Synergy | Novelty Validation