📢 Excited to Share Our New Survey on 3D-LLMs! This work is a collaboration between researchers from @UniofOxford, @TU_Muenchen, @KAUST_News, @mbzuai, @SFU, and @ETH_en. 📄Paper: https://t.co/lOkelXK9tE 🔗Project: https://t.co/fyJ2MzY7Fz #AI #LLM #3D 🧵

📢A new learning-based approach to SfM: #ACEZero No img-to-img matching, optimises image-to-scene correspondences directly. Needs no pose priors. Works on unordered image sets. Efficiently handles thousands of images. Paper: https://t.co/Zc83YpoXpY Page: https://t.co/kutgb81eFV

🔥Map-relative Pose Regression🔥(#CVPR2024 highlight) For years absolute pose regression did not work. There was some success by massively synthesising scene-specific data. We train scene-agnostic APR and it works. Paper: https://t.co/MMHLYtS3Ay Page: https://t.co/djb1jPjwbh

📢 Our #paper "Neural Refinement for Absolute Pose Regression with Feature Synthesis" has been accepted to #CVPR 2024! 🥂 Congratulations to the team - @ShuaiC8, Victor, Xinghui, Jia-Wang, @kejie_li, @ziruiwang_ 🙌 #cvpr2024 #ai #machinelearning #3D #computervision #research

Our paper "DFNet: Enhance Absolute Pose Regression with Direct Feature Matching" was accepted by #ECCV2022. It was a joint work with my @AVLOxford colleagues @li_xinghui, @ziruiwang_ox, and Prof. Victor A. Prisacariu. Paper: https://t.co/CR8GV6D2AX Code:

Checkout our 3DV 2021 paper Direct-PoseNet: Absolute Pose Regression with Photometric Consistency and our released code at Paper: https://t.co/t5IumAOEqx Code: https://t.co/wa9g8oDDXP Project: https://t.co/bNjs4isRlI w/ @ziruiwang_ox @viprad @AVLOxford