How do you decompose a 2D image into accurate 3D object detections? You use🥊Boxer. A new model from Reality Labs Research enables robust 3D object detection by "lifting" 2D proposals from off-the-shelf detectors like OWL-ViT and SAM into metric 3D space. No more "flat" AI—this is about spatial intelligence for the next generation of wearables. Blog🔗 projectaria.com/news/introdu… Website with links to download: facebookresearch.github.io/b… 👉@ddetone

I implemented it and the ~8 degree gravity correction from GeoCalib made a real difference. Look at the monitor - on the left (pose heuristic) the box is tilted and doesn't match the screen edges, on the right (GeoCalib) it wraps the monitor much more tightly. The shelf boxes at the top are also cleaner, less overshoot. Yeah, the improvement is clear.

Introducing ShapeR, a method for robust conditional 3D shape generation from casually captured sequences. ShapeR leverages a rectified flow transformer conditioned on per-object multimodal data to turn casual image sequences into full metric scene reconstructions. Project Page: facebookresearch.github.io/S… Paper: arxiv.org/abs/2601.11514 Links to code and huggingface below ⬇️

📢Sonata: Self-Supervised Learning of Reliable Point Representations📢 Meet Sonata, our"3D-DINO" pre-trained with Point Transformer V3, accepted at #CVPR2025! 🌍: xywu.me/sonata 📦: github.com/facebookresearch/… 🚀: github.com/Pointcept/Pointce… 🔹Semantic-aware and spatial reasoning representations learned with no label; 🔹3x linear probing accuracy (from 21.8% to 72.5%) on ScanNet; 🔹2x data efficiency performance with only 1% of the data compared to previous approaches; 🔹As always, establish new SOTA results across indoor and outdoor 3D perception tasks. Our author team: @HengshuangZhao, @jstraub6, @rapideRobot, @ddetone, @NinjaDuncan, @TianweiS, @Christopher_Xie, @NanYang719.