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#

🧊 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#

In China a smart spraying system uses computer vision and artificial intelligence inside cattle barns, accurately detecting the cows’ positions and needs. This technology helps reduce water consumption by about 60% and significantly lowers waste‑processing and fertilizer costs — boosting operational efficiency and increasing the profitability of dairy farms.

Drawing bounding boxes is the easy part. Building a scalable, enterprise-ready computer vision platform is where the real engineering begins. We walk you through a reference architecture for GPU-accelerated AI camera analytics on OCI, designed to process smart camera events, IP camera streams, images, and video clips at scale.

Why have only 6% of U.S. manufacturers adopted robots? Because they've been too hard to use, and not capable enough. Until now. AI-native robots are changing everything. With our newest AI feature ClickFind, you can train your robot in seconds to see and track objects in real-time with just a few taps. Perfect for rapid deployment in high-mix manufacturing environments, no PhD in computer vision required. Watch the live demo from our @AutomateShow keynote and see for yourself.