👁️ What if every camera could become an intelligent eye? That is the promise of Computer Vision + AI. With an AI Box, intelligence can move closer to the camera — enabling video streams to be analysed locally for events, patterns and actionable insights. From security & surveillance to retail, transportation, infrastructure and smart cities, the evolution is happening: Camera → AI Box → Computer Vision → Insight → Action At Transline, we are bringing together CCTV, Biometrics, AI, Computer Vision and Edge AI through our technology ecosystem and NETRA stack. The future isn't just about capturing more video. It's about understanding what the video means. See. Understand. Respond. The camera is becoming a sensor. The AI Box is becoming the brain. And Computer Vision is becoming the intelligence layer. 👁️🧠 #ComputerVision# #AI# #EdgeAI# #AIBox# #VideoAnalytics# #CCTV# #Biometrics# #NETRA# #ArtificialIntelligence# #DigitalIndia#

🧵 New repo: Awesome Egocentric Dataset — a curated list of first-person vision datasets, refreshed beyond the classic Ego4D-era list. ✅ Recent 2023–2026 releases (Ego-Exo4D, EgoSchema, AEA, EgoExoLearn, EgoLife…) ✅ Dead/broken links removed, migrated ones fixed ✅ A section on egocentric-driven VLA papers (training source + data-processing), with first-principles + fishbone analysis 🚀 Open-source, contributions welcome. 🔗 #EgocentricVision# #FirstPersonVision# #VLA# #ComputerVision# #Datasets#

TL;DR FoundationStereo, the high-accuracy zero-shot stereo model, sped up by over 10x with almost no accuracy loss. It finally runs in real time. Title: Fast-FoundationStereo: Real-Time Zero-Shot Stereo Matching URL: Points 🎯 Problem: stereo had split into "accurate but heavy foundation models" vs "fast methods that sacrifice accuracy and generalization" 🧩 Divide-and-conquer: each of three bottlenecks accelerated separately (feature extraction, cost filtering, refinement) 📚 Feature extraction: distill hybrid monocular + stereo priors into a single efficient student 🔍 Cost filtering: blockwise NAS over 8 blocks cuts search from O(n^N) to O(n), with ILP minimizing error under a latency budget ✂️ Refinement: structured pruning via ConvGRU recurrent dependency graphs and Taylor-expansion importance 🗂 1.4M in-the-wild stereo pairs auto pseudo-labeled with a normal-consistency check ⚡ Middlebury-Q: 4.51% BP-2 at 49ms (vs 2.64% at 496ms, 10.1x slower), down to 21ms with TensorRT A new SOTA among real-time stereo methods, well suited for robotics and AR deployment. #StereoMatching# #ComputerVision#

We were honored to host Dr. Nadia Brancati(NRCI), for a guest seminar on May 28th! 🇮🇹 She shared incredible insights from her cutting-edge work in Computer Vision, Machine Learning, and Medical Imaging. Thank you for an inspiring talk! #ComputerVision# #MedicalAI# #MachineLearning#

Seeing InternVideo-Next explicitly characterize its architecture as "Encoder–Predictor–Decoder (EPD)" and defining the Predictor as a "latent world model" confirms a clear convergence in the field. 📉 From our early work on Context AutoEncoder (CAE, — where we decoupled the Encoder, Predictor ("Regressor" in the paper), and Decoder — to Meta's I-JEPA/V-JEPA shifting entirely to latent prediction, and now this. It seems we are all validating the same core intuition: Understanding ≠ Pixel Reconstruction. 💡 Decoupling representation learning from high-frequency detail generation to build World Models in latent space is evidently the path forward. Glad to see our early intuition resonating with the latest SOTA. 🫡 #AI# #ComputerVision# #WorldModel# #JEPA# #CAE#

The Vesuvius Challenge is still a CHALLENGE! 🧠📜 We’re teaming up with @kaggle to launch a $100K Surface Detection competition on 3D CT scans of the Herculaneum scrolls: . Train topology-aware models, climb the leaderboard, and help us read an ancient library! #VesuviusChallenge# #kaggle# #computervision# #machinelearning#

🧊 Turning one image into 3D used to force a choice: "accurate on the visible surface but no backside" or "complete but misaligned with the input." World Tracing stacks 3D points per pixel into layers, capturing visible and hidden surfaces at once. Title: World Tracing: Generative Pixel-Aligned Geometry Beyond the Visible URL: 🔍 Overview World Tracing represents geometry as an ordered stack of L camera-space 3D points per pixel. Layer 0 is the visible surface, deeper layers record front-to-back intersections with surfaces hidden behind the foreground, unifying faithful reconstruction and generative completion as one layered problem. ❓ Challenges Solved Image-to-3D carried a fundamental trade-off. ・Depth estimators are pixel-accurate but stop at the visible surface ・Generative 3D models are complete but work in canonical frames, so they misalign with the input World Tracing frames this as faithful generation: accurately reconstruct the visible surface while plausibly generating the invisible. 💡 Methodology & Proposed Approach At its core is WT-DiT, a 1.7B-parameter diffusion transformer. ・Three-way factorized attention (layer-wise, ray-wise, global) preserves depth ordering and front-to-back coherence ・A mixed noise schedule handles the asymmetry between layer 0 (image-constrained, reconstruction-like) and deeper, generative layers by varying noise per layer ・Mix-training lets multilayer (3D assets) and single-layer (RGBD photos) supervision train together 🎯 Use Cases ・Text-driven 3D scene editing (training-free closed-form compositing thanks to pixel alignment) ・Geometry-conditioned novel-view video synthesis using complete hidden geometry as memory ・A TRELLIS hybrid that yields faithful meshes which reproject correctly to the input 📊 Experimental Results It outperforms prior work on object, scene, and dynamic benchmarks. ・Object visible-depth MAE 0.0149 (VGGT 0.0257) ・Complete-shape F-score@0.05 0.549 (TRELLIS 0.204) ・Scene MAE 0.0102, and best dynamic-clip Chamfer L2 at 0.0105 #3DGeneration# #ComputerVision#