Not Your Stereo-Typical Estimator: Combining Vision and Language for Volume Perception

• URL: http://arxiv.org/abs/2604.09886v1
• Date: Fri, 10 Apr 2026 20:24:57 GMT
• Title: Not Your Stereo-Typical Estimator: Combining Vision and Language for Volume Perception
• Abstract summary: We introduce a new method that fuses implicit 3D cues from stereo vision with explicit prior knowledge from natural language text.<n>Our approach extracts deep features from a stereo image pair and a descriptive text prompt that contains the object's class and an approximate volume.<n>We conduct extensive experiments on public datasets demonstrating that our text-guided approach significantly outperforms vision-only baselines.
• Score: 9.480328219158254
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate volume estimation of objects from visual data is a long-standing challenge in computer vision with significant applications in robotics, logistics, and smart health. Existing methods often rely on complex 3D reconstruction pipelines or struggle with the ambiguity inherent in single-view images. To address these limitations, we introduce a new method that fuses implicit 3D cues from stereo vision with explicit prior knowledge from natural language text. Our approach extracts deep features from a stereo image pair and a descriptive text prompt that contains the object's class and an approximate volume, then integrates them using a simple yet effective projection layer into a unified, multi-modal representation for regression. We conduct extensive experiments on public datasets demonstrating that our text-guided approach significantly outperforms vision-only baselines. Our findings show that leveraging even simple textual priors can effectively guide the volume estimation task, paving the way for more context-aware visual measurement systems. Code: https://gitlab.com/viper-purdue/stereo-typical-estimator.

Related papers

• Assisted Spatial Cognition Through Vision-Language Models [0.0]
  Multimodal AI is transforming assistive technologies by enabling simultaneous processing of visual and textual data.<n>This advancement holds significant promise for over 43 million visually impaired and neuro-divergent individuals worldwide.<n>We introduce a novel end-to-end framework that integrates Large Language Models (LLMs) and Vision-Language Models (VLMs)<n>Our system captures video input via standard mobile phone cameras, and employs SLAM3R to generate dense 3D point clouds in real-time.
  arXiv  Detail & Related papers  (2026-09-11T11:58:59Z)
• ViQ: Text-Aligned Visual Quantized Representations at Any Resolution [91.46185855575789]
  We present ViQ, a Visual Quantized Representations framework, which is designed to balance semantics and details in discrete representations.<n>Our approach structures quantization learning into two stages: text-aligned pre-training and feature discretization.<n>Experiments on multimodal tasks demonstrate that ViQ achieves competitive performance compared to state-of-the-art multimodal vision encoders.
  arXiv  Detail & Related papers  (2026-06-25T17:29:27Z)
• VirPro: Visual-referred Probabilistic Prompt Learning for Weakly-Supervised Monocular 3D Detection [12.835071167163607]
  VirPro is an adaptive multi-modal pretraining paradigm that can be seamlessly integrated into diverse weakly supervised monocular 3D detection frameworks.<n>We generate a diverse set of learnable, instance-conditioned prompts across scenes and store them in an Adaptive Prompt Bank (APB)
  arXiv  Detail & Related papers  (2026-03-18T08:23:55Z)
• Visual Jigsaw Post-Training Improves MLLMs [58.29961336087896]
  We introduce Visual Jigsaw, a generic self-supervised post-training framework designed to strengthen visual understanding in large language models (MLLMs)<n>Visual Jigsaw is formulated as a general ordering task: visual inputs are partitioned, shuffled, and the model must reconstruct the visual information by producing the correct permutation in natural language.<n>Extensive experiments demonstrate substantial improvements in fine-grained perception, temporal reasoning, and 3D spatial understanding.
  arXiv  Detail & Related papers  (2025-09-29T17:59:57Z)
• IR3D-Bench: Evaluating Vision-Language Model Scene Understanding as Agentic Inverse Rendering [7.247417417159471]
  Vision-language models (VLMs) excel at descriptive tasks, but whether they truly understand scenes from visual observations remains uncertain.<n>We introduce IR3D-Bench, a benchmark challenging VLMs to demonstrate understanding through active creation rather than passive recognition.
  arXiv  Detail & Related papers  (2025-06-29T17:02:57Z)
• Revisit What You See: Disclose Language Prior in Vision Tokens for LVLM Decoding [6.612630497074871]
  Large Vision-Language Models (LVLMs) achieve strong performance across multimodal tasks by integrating visual perception with language understanding.<n>We propose ReVisiT, a training-free decoding method that references vision tokens to guide text generation.
  arXiv  Detail & Related papers  (2025-06-11T08:46:55Z)
• Lyrics: Boosting Fine-grained Language-Vision Alignment and Comprehension via Semantic-aware Visual Objects [11.117055725415446]
  Large Vision Language Models (LVLMs) have demonstrated impressive zero-shot capabilities in various vision-language dialogue scenarios. The absence of fine-grained visual object detection hinders the model from understanding the details of images, leading to irreparable visual hallucinations and factual errors. We propose Lyrics, a novel multi-modal pre-training and instruction fine-tuning paradigm that bootstraps vision-language alignment from fine-grained cross-modal collaboration.
  arXiv  Detail & Related papers  (2023-12-08T09:02:45Z)
• One at a Time: Progressive Multi-step Volumetric Probability Learning for Reliable 3D Scene Perception [59.37727312705997]
  This paper proposes to decompose the complicated 3D volume representation learning into a sequence of generative steps. Considering the recent advances achieved by strong generative diffusion models, we introduce a multi-step learning framework, dubbed as VPD. For the SSC task, our work stands out as the first to surpass LiDAR-based methods on the Semantic KITTI dataset.
  arXiv  Detail & Related papers  (2023-06-22T05:55:53Z)
• Contextual Object Detection with Multimodal Large Language Models [66.15566719178327]
  We introduce a novel research problem of contextual object detection. Three representative scenarios are investigated, including the language cloze test, visual captioning, and question answering. We present ContextDET, a unified multimodal model that is capable of end-to-end differentiable modeling of visual-language contexts.
  arXiv  Detail & Related papers  (2023-05-29T17:50:33Z)
• ALSO: Automotive Lidar Self-supervision by Occupancy estimation [70.70557577874155]
  We propose a new self-supervised method for pre-training the backbone of deep perception models operating on point clouds. The core idea is to train the model on a pretext task which is the reconstruction of the surface on which the 3D points are sampled. The intuition is that if the network is able to reconstruct the scene surface, given only sparse input points, then it probably also captures some fragments of semantic information.
  arXiv  Detail & Related papers  (2022-12-12T13:10:19Z)
• A Simple Long-Tailed Recognition Baseline via Vision-Language Model [92.2866546058082]
  The visual world naturally exhibits a long-tailed distribution of open classes, which poses great challenges to modern visual systems. Recent advances in contrastive visual-language pretraining shed light on a new pathway for visual recognition. We propose BALLAD to leverage contrastive vision-language models for long-tailed recognition.
  arXiv  Detail & Related papers  (2021-11-29T17:49:24Z)

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.