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20242026
most citedNeural Implicit Representation for Building Digital Twins of Unknown Articulated Objects

2 citations · 4 across the 17 of their papers we have counts for

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10 papers · 1 filter

cs.CV2026

MG-VQA: Manipulation Grounded Visual Question Answering with VLMs

Vineet Bhat, Siyi Chen, Mikaela Angelina Uy +6

Vision-language models (VLMs) have shown promising spatial reasoning capabilities from static visual inputs, where the evidence needed to answer a question is available in the prov…

cs.CV2026

Effective Multi-sensor Conditioning for Street-view Novel-view Synthesis

Zhengfei Kuang, Adam Sun, Liyuan Zhu +7

Modern vehicle platforms are equipped with a rich sensor suite, including LiDAR, calibrated multi-camera rigs, and accurate ego-motion, that in principle offers strong signal for r…

cs.CV2026

Why Far Looks Up: Probing Spatial Representation in Vision-Language Models

Cheolhong Min, Jaeyun Jung, Daeun Lee +5

Vision-language models (VLMs) achieve strong performance on spatial reasoning benchmarks, yet it remains unclear whether this reflects structured 3D understanding or reliance on st…

cs.CV2026

MME-CoF-Pro: Evaluating Reasoning Coherence in Video Generative Models with Text and Visual Hints

Yu Qi, Xinyi Xu, Ziyu Guo +10

Video generative models show emerging reasoning behaviors. It is essential to ensure that generated events remain causally consistent across frames for reliable deployment, a prope…

cs.CV2025

SpaceTools: Tool-Augmented Spatial Reasoning via Double Interactive RL

Siyi Chen, Mikaela Angelina Uy, Chan Hee Song +6

Vision Language Models (VLMs) demonstrate strong qualitative visual understanding, but struggle with metrically precise spatial reasoning required for embodied applications. The ag…

cs.CV2025

BOP-ASK: Object-Interaction Reasoning for Vision-Language Models

Vineet Bhat, Sungsu Kim, Valts Blukis +6

Vision Language Models (VLMs) have achieved impressive performance on spatial reasoning benchmarks, yet these evaluations mask critical weaknesses in understanding object interacti…

cs.CV2024

RoboSpatial: Teaching Spatial Understanding to 2D and 3D Vision-Language Models for Robotics

Chan Hee Song, Valts Blukis, Jonathan Tremblay +3

Spatial understanding is a crucial capability that enables robots to perceive their surroundings, reason about their environment, and interact with it meaningfully. In modern robot…

cs.CV2024

NeRFDeformer: NeRF Transformation from a Single View via 3D Scene Flows

Zhenggang Tang, Zhongzheng Ren, Xiaoming Zhao +4

We present a method for automatically modifying a NeRF representation based on a single observation of a non-rigid transformed version of the original scene. Our method defines the…

cs.CV2024★ 2 cited

Neural Implicit Representation for Building Digital Twins of Unknown Articulated Objects

Yijia Weng, Bowen Wen, Jonathan Tremblay +4

We address the problem of building digital twins of unknown articulated objects from two RGBD scans of the object at different articulation states. We decompose the problem into tw…

cs.CV2024★ 1 cited

Snap-it, Tap-it, Splat-it: Tactile-Informed 3D Gaussian Splatting for Reconstructing Challenging Surfaces

Mauro Comi, Alessio Tonioni, Max Yang +5

Touch and vision go hand in hand, mutually enhancing our ability to understand the world. From a research perspective, the problem of mixing touch and vision is underexplored and p…