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Robbyant releases LingBot-Vision, a 1B-parameter vision model trained on boundaries that achieves better depth estimation than DINOv3-7B, with open weights.
This paper proposes masked depth modeling with sensor-validity masking, achieving best RMSE on 7 out of 8 masked/sparse depth benchmarks, with a controlled encoder-init study.
LingBot-Depth 2.0, trained on 150M samples, solves the longstanding problem of robots being blind to glass and transparent objects, achieving top performance on 12/16 depth benchmarks and halving depth error. Ant Group used it to significantly improve their robots' perception.
Robbyant, an embodied AI company under Ant Group, released LingBot-Vision, a self-supervised vision backbone family ranging from 21M to 1.1B parameters, under Apache-2.0. It matches or beats DINOv3 on several depth and segmentation benchmarks despite using less than one third of the training data, highlighting a push for open perception models.
Ant Group released LingBot-Vision, a family of DINO-style vision backbones in 4 sizes; the 0.3B ViT-L matches DINOv3-7B on NYUv2 depth with ~23x fewer parameters, showcasing significant efficiency gains.
This paper introduces masked boundary modeling, a self-supervised paradigm for vision pretraining that learns sub-pixel boundary representations to improve dense spatial perception. The resulting model, LingBot-Vision, demonstrates significant improvements in depth estimation and other downstream tasks, showing that boundary modeling is a scalable pretraining principle for spatially structured visual representations.
Introduces MultiDepth-3k, a benchmark to evaluate depth-layer preferences in monocular depth foundation models, and shows Laplacian Visual Prompting can alter reported depth layers, suggesting complementary geometric hypotheses exist across models.
MMDiff extends frozen diffusion transformers into multi-modal generative systems using lightweight decoders, achieving significant improvements in semantic segmentation and other perceptual tasks through multi-timestep feature fusion.
Bardienus Duisterhof introduces Modality Forcing, a recipe for post-training text-to-image (T2I) models that achieves state-of-the-art results on 4 out of 5 monocular depth estimation benchmarks.
αDepth introduces a layered representation with Circular Alpha Representation (CAR) to address soft boundary challenges in stereo conversion, achieving state-of-the-art performance without manual guidance.
This paper proposes VLM3, a method that adapts vision language models for 3D understanding tasks through simple architectural modifications and text-based training, achieving competitive performance without complex designs. It demonstrates significant improvements in depth estimation accuracy and enables diverse 3D tasks like pixel correspondence, camera pose estimation, and object-level understanding.
ViGeo is a transformer-based foundation model that recovers dense and consistent 3D geometry from videos using dynamic chunking attention and a completion-based data refinement framework, achieving state-of-the-art performance across multiple tasks.
This paper proposes GASP, a framework that injects geometric priors into vision-language models via deep supervision with contrastive and depth consistency losses, achieving significant improvements on 3D spatial reasoning benchmarks without using 3D VQA data.
PaGeR adapts the multi-view perspective foundation model Depth Anything 3 to predict scale-invariant and metric depth, surface normals, and sky segmentation from a single equirectangular image, using a fixed cubemap representation that keeps VRAM and runtime constant. The paper also releases the ZüriPano and PanoInfinigen datasets.
DepthVLM enhances Vision-Language Models with a lightweight depth head and unified vision-text supervision, achieving dense metric depth estimation and improved 3D spatial reasoning while maintaining multimodal capabilities.